Riccardo De Bin

• Reiersrud, Hedvig Borgen; Salucci, Clara Bertinelli; Kvellestad, Anders & Bin, Riccardo De (2026). Confidence Curve Combination as an Alternative to Wilks in High-Energy Physics. In Bin, Riccardo De; Salucci, Clara Bertinelli & Hubin, Aliaksandr (Ed.), Proceedings of the 40th International Workshop on Statistical Modelling 28.06 – 03.07.2026, Oslo, Norway. International Workshop on Statistical Modelling. ISSN 9788255325000. p. 174–179. Full text in Research Archive Show summary

  In high-energy physics, inference for parameters of interest is commonly based on Wilks’ theorem, using the asymptotic chi-square pivot of the profile likelihood ratio to perform hypothesis testing or construct confidence intervals. However, the validity of this approximation relies on asymptotics and regularity conditions that may fail in practice, leading to miscoverage of the derived confidence intervals in nonregular or small-sample settings. We propose an alternative approach based on confidence curves, which provides coverage-exact inference without relying on asymptotic approximations. Results from a simulation study show that Wilks-based intervals can severely overcover in small-signal scenarios, while our method maintains nominal coverage for all confidence levels.

• Cibinel, Luca; Linjordet, Trond; Pensar, Johan; Badia, David Balcells; Bin, Riccardo De & Ell, Basil (2026). tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes. Journal of Chemical Information and Modeling. ISSN 1549-9596. 66(13), p. 7524–7538. doi: 10.1021/acs.jcim.6c01281. Full text in Research Archive Show summary

  Transition metal complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible data sets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modeling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) data set, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 60k TMCs. These descriptions are both qualitative and quantitative, encompassing the compositional nature of TMCs as well as the entirety of their molecular graphs. The Python package tmqmrdfdata is released together with the knowledge graph to provide a user-friendly interface. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models.

• Salucci, Clara Bertinelli; Crujeiras, Rosa M.; Bin, Riccardo De; Durbán, Maria; Gijbels, Irène & Kneib, Thomas [Show all 8 contributors for this article] (2026). Discussion on: Shape constrained additive smooth models by Simon N. Wood. Statistical Modelling. ISSN 1471-082X. doi: 10.1177/1471082x261447615. Full text in Research Archive
• Meggio, Claudio; Pensar, Johan; Badia, David Balcells & Bin, Riccardo De (2026). A path-based boosting algorithm for exploring transition metal compounds. Statistical Modelling. ISSN 1471-082X. doi: 10.1177/1471082x251407458. Full text in Research Archive Show summary

  Motivated by the analysis of chemical metal compounds and their properties for catalysis, we developed a gradient boosting model that explores graph structures to perform prediction tasks. Taking advantage of the iterative nature of boosting, our novel approach, called PathBoost , explores the graphs to identify the relevant paths and simultaneously fits a prediction model. Advantages of PathBoost include automatic variable selection, as only relevant paths are kept in the model and explainability, as a measure of variable importance is provided. The novel algorithm is applied to the tmQM dataset, where the molecules are represented as graphs with atoms as nodes and bonds as edges. The goal is to predict specific quantum properties of the molecules, such as the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) gap. These properties usually require heavy computational power to be computed, while our model aims to provide comparable results using much fewer resources.

• Salucci, Clara Bertinelli; Kvellestad, Anders & Bin, Riccardo De (2025). A note on the asymptotic distribution of the Likelihood Ratio Test statistic under boundary conditions. arXiv. doi: https:/arxiv.org/pdf/2509.00223. Full text in Research Archive Show summary

  In the context of likelihood ratio testing with parameters on the boundary, we revisit two situations for which there are some discrepancies in the literature: the case of two parameters of interest on the boundary, with all other parameters in the interior, and the case where one of the two parameters on the boundary is a nuisance parameter. For the former case, we clarify that two seemingly conflicting results are consistent upon closer examination. For the latter, we clarify the source of the discrepancy and explain the different findings. As for this case the closed-form expression is valid only under positive correlation, we further propose a heuristic modification to the asymptotic distribution of the likelihood ratio test that extends its applicability to cases involving negative correlation.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Lindqvist, Bo Henry; Vanem, Erik & Bin, Riccardo De (2025). Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process. Lifetime Data Analysis. ISSN 1380-7870. 31, p. 300–339. doi: 10.1007/s10985-025-09648-z. Full text in Research Archive
• Hellton, Kristoffer Herland; Lingjærde, Camilla & Bin, Riccardo De (2024). Influence of single observations on the choice of the penalty parameter in ridge regression. Journal of Statistical Computation and Simulation. ISSN 0094-9655. doi: 10.1080/00949655.2024.2440542. Full text in Research Archive Show summary

  Penalized regression methods such as ridge regression heavily rely on the choice of the penalty parameter, which is often computed via cross-validation. Discrepancies in the value of the penalty parameter may lead to substantial differences in regression coefficient estimates and predictions. This paper investigates the effect of single observations on the optimal choice of the penalty parameter, showing how the presence of influential points can change it dramatically. The points are distinguished as ‘expanders’ or ‘shrinkers’ based on their effect on the model complexity. The approach supplies a visual exploratory tool to identify influential points, naturally implementable for high-dimensional data where traditional approaches usually fail. Applications to simulated and real data examples, both low- and high-dimensional, are presented. The visual tool is implemented in the R package influridge.

• Vanem, Erik; Gramstad, Odin; Babanin, Alexander; Bin, Riccardo De & Trulsen, Karsten (2024). On the distribution of ocean wave crest heights in varying wave conditions. Journal of Ocean Engineering and Marine Energy. ISSN 2198-6444. 10(4), p. 797–815. doi: 10.1007/s40722-024-00350-0. Full text in Research Archive Show summary

  This paper addresses the statistical distribution of wave crest heights in ocean environments. This is much needed in several engineering applications including risk and reliability assessment of marine and coastal structures and is an important input for design of ocean structures. However, even though crest height distributions have received a lot of attention within both academia and the industry, current state-of-the-art models have notable shortcomings and do not describe the distribution of crest heights well in all sea states. In particular, commonly used models do not accurately describe the upper tail in certain unstable wave conditions. This will be addressed in this paper, which investigates in what sea-state conditions, established crest height distributions describe the data well and under what conditions, these models fail to fit well to the data. A large dataset of crest heights observed in different sea states is analyzed, and various goodness-of-fit tests are applied to determine when the different statistical models are appropriate. Then rejection rates of the various statistical models will be linked to selected sea-state parameters to identify wave conditions where alternative statistical models are needed. Results from this study indicate that the well-known Forristall distribution, which is known to capture second-order effects well, fails to perform well in some sea states. Different variants of the Tayfun–Fedele distribution are also analyzed, and results suggest that this represent a slight improvement. Somewhat surprisingly, sea-state parameters such as significant wave height (Hs), significant wave steepness (S1), and the Benjamin–Feir index (BFI) do not seem to be very influential on the probability of rejecting these distributions, even though empirical skewness and kurtosis play a significant role. The implications of this for describing the distribution of crest heights well in certain sea-state conditions are discussed.

• Ottenbreit, Mathias von & Bin, Riccardo De (2024). Automatic piecewise linear regression. Computational statistics (Zeitschrift). ISSN 0943-4062. 39, p. 1867–1907. doi: 10.1007/s00180-024-01475-4. Full text in Research Archive Show summary

  Abstract Regression modelling often presents a trade-off between predictiveness and interpretability. Highly predictive and popular tree-based algorithms such as Random Forest and boosted trees predict very well the outcome of new observations, but the effect of the predictors on the result is hard to interpret. Highly interpretable algorithms like linear effect-based boosting and MARS, on the other hand, are typically less predictive. Here we propose a novel regression algorithm, automatic piecewise linear regression (APLR), that combines the predictiveness of a boosting algorithm with the interpretability of a MARS model. In addition, as a boosting algorithm, it automatically handles variable selection, and, as a MARS-based approach, it takes into account non-linear relationships and possible interaction terms. We show on simulated and real data examples how APLR’s performance is comparable to that of the top-performing approaches in terms of prediction, while offering an easy way to interpret the results. APLR has been implemented in C++ and wrapped in a Python package as a Scikit-learn compatible estimator.

• Belhechmi, Shaima; Teuff, Gwénaël Le; Bin, Riccardo De; Rotolo, Federico & Michiels, Stefan (2023). Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine. BMC Bioinformatics. 24(1). doi: 10.1186/s12859-023-05162-x. Full text in Research Archive Show summary

  Background The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint. Results To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect. Conclusions Adaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.

• Hubin, Aliaksandr; Heinze, Georg & Bin, Riccardo De (2023). Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models. Fractal and Fractional. 7(9), p. 1–23. doi: 10.3390/fractalfract7090641. Full text in Research Archive Show summary

  We propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility.

• Kneiding, Hannes; Lukin, Ruslan; Lang, Lucas; Reine, Simen; Pedersen, Thomas Bondo & Bin, Riccardo De [Show all 7 contributors for this article] (2023). Deep learning metal complex properties with natural quantum graphs. Digital Discovery. 2(3), p. 618–633. doi: 10.1039/d2dd00129b. Full text in Research Archive Show summary

  Machine learning can make a strong contribution to accelerating the discovery of transition metal complexes (TMC). These compounds will play a key role in the development of new technologies for which there is an urgent need, including the production of green hydrogen from renewable sources. Despite the recent developments in machine learning for drug discovery and organic chemistry in general, the application of these methods to TMCs remains challenged by their higher complexity and the limited availability of large datasets. In this work, we report a representation for deep graph learning on TMCs – the natural quantum graph (NatQG), which leverages the electronic structure data available from natural bond orbital (NBO) analysis. This data was used to define both the topology and the information expressed by the NatQG graphs. At the topology level, two different NatQG flavors were developed: u-NatQG, with undirected edges, and d-NatQG, with edges directed along donor → acceptor orbital interactions. At the information level, the node and edge attribute vectors of both graphs contain NBO data, including natural charges and bond orders. The NatQG graphs were used to develop graph neural networks (GNNs) for the prediction of the quantum properties underlying the structure and reactivity of TMCs (e.g. HOMO–LUMO gap and polarizability). These models surpassed baselines based on traditional descriptors and performed at a level similar to, or higher than, state-of-the-art GNNs based on radial cutoffs. The results showed that the electronic structure information encoded by the models has a stronger impact on its accuracy than the geometric information. With the aim of benchmarking the GNNs, we also developed the transition metal quantum mechanics graph dataset (tmQMg), which provides the geometries, properties, and NatQG graphs of 60k TMCs.

• Rahnenführer, Jörg; Bin, Riccardo De; Benner, Axel; Ambrogi, Federico; Lusa, Lara & Boulesteix, Anne-Laure [Show all 11 contributors for this article] (2023). Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges. BMC Medicine. 21. doi: 10.1186/s12916-023-02858-y. Full text in Research Archive Show summary

  Background In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. Methods Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 “High-dimensional data” of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. Results The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. Conclusions This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2023). A novel semi-supervised learning approach for State of Health monitoring of maritime lithium-ion batteries. Journal of Power Sources. ISSN 0378-7753. 556. doi: 10.1016/j.jpowsour.2022.232429. Full text in Research Archive Show summary

  Lithium-ion batteries are a prominent technology for the electrification of the transport sector, which itself is a key measure towards the departure from fossil fuels. The “green shift” is taking place in the marine industry too, where the number of battery-powered vessels is fastly growing. In this case, monitoring the battery State of Health is essential more than ever to optimise battery use, promote safety, and ensure the coverage of ship power and energy demands. Classification societies typically require annual capacity tests for this purpose; however, the tests are disruptive, costly and time-consuming. As a consequence they are seldom, in addition to not being always fully reliable. We propose a novel alternative semi-supervised learning approach to estimate the State of Health of a lithium-ion battery system with no labelled data, starting from a minimal set of weakly labelled data from another similar system. The method is based on operational sensor data gathered from the battery, together with the battery State of Charge. Our results show that the procedure is valid, and the obtained estimates can be used to significantly progress in failure prevention, operational optimisation, and for planning batteries at the design stage.

• Cao, Yang; Moe, S. Jannicke; Bin, Riccardo De; Tollefsen, Knut-Erik & Song, You (2023). Comparison of piecewise structural equation modeling and Bayesian network for de novo construction of a quantitative adverse outcome pathway network. ALTEX - Alternatives to Animal Experimentation. ISSN 1868-596X. 40(2), p. 287–298. doi: 10.14573/altex.2207113. Full text in Research Archive Show summary

  Quantitative adverse outcome pathway network (qAOPN) is gaining momentum due to the predictive nature, alignment with quantitative risk assessment and great potential as a computational new approach methodology (NAM) to reduce laboratory animal tests. The present work aimed to demonstrate two advanced modeling approaches, piecewise structural equation modeling (PSEM) and Bayesian network (BN), for de novo qAOPN model construction based on routine ecotoxicological data. A previously published AOP network comprised of four linear AOPs linking excessive reactive oxygen species production to mortality in aquatic organisms was employed as a case study. The demonstrative case study intended to answer: Which linear AOP in the network contributed the most to the AO? Can any of the upstream KEs accurately predict the AO? What are the advantages and limitations of PSEM or BN in qAOPN development? The outcomes from the two approaches showed that both PSEM and Bayesian network were suitable for constructing a complex qAOPN based on limited experimental data. Besides quantification of response-response relationships, both approaches were capable of identifying the most influencing linear AOP in a complex network and evaluating the predictive ability of the AOP, albeit some discrepancies in predictive ability were identified for the two approaches using this specific dataset. The PROs and CONs of the two approaches for qAOPN construction were discussed in detail and suggestions on optimal workflows of PSEM and BN were provided to guide future qAOPN development.

• Bin, Riccardo De & Stikbakke, Vegard (2022). A boosting first-hitting-time model for survival analysis in high-dimensional settings. Lifetime Data Analysis. ISSN 1380-7870. doi: 10.1007/s10985-022-09553-9. Full text in Research Archive Show summary

  In this paper we propose a boosting algorithm to extend the applicability of a first hitting time model to high-dimensional frameworks. Based on an underlying stochastic process, first hitting time models do not require the proportional hazards assumption, hardly verifiable in the high-dimensional context, and represent a valid parametric alternative to the Cox model for modelling time-to-event responses. First hitting time models also offer a natural way to integrate low-dimensional clinical and high-dimensional molecular information in a prediction model, that avoids complicated weighting schemes typical of current methods. The performance of our novel boosting algorithm is illustrated in three real data examples.

• Midtfjord, Alise Danielle; Bin, Riccardo De & Huseby, Arne Bang (2022). A boosting model for survival analysis with dependent censoring. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. Full text in Research Archive
• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2022). A novel semi-supervised learning approach for maritime lithium-ion battery monitoring. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. p. 85–90. Full text in Research Archive Show summary

  We propose a novel semi-supervised learning method to monitor the State of Health of lithium-ion batteries, a prominent technology for the electrification of the transport sector. Our approach enables State of Health monitoring of batteries with no labeled data, starting from a minimal set of labeled data from another similar battery. This can be achieved by exploiting the relation between a pseudo-capacity measure and the total capacity of the labeled data. Our results with operational data from maritime batteries show that the approach is valid and can lead to significant progress in failure prevention, operational optimization, and for planning batteries at the design stage.

• Salucci, Clara Bertinelli; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & Bin, Riccardo De (2022). Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions. Journal of Energy Storage. ISSN 2352-152X. 52. doi: 10.1016/j.est.2022.104903. Full text in Research Archive Show summary

  Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery capacity degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models: they rely solely on the available cycling data, while remaining agnostic to the underlying real physical processes. This paper proposes a data-driven approach which is understudied in the context of battery degradation, despite being characterised by simplicity and ease of computation: the Multivariable Fractional Polynomial (MFP) regression. Models are trained from historical data of one exhausted cell and used to predict the SoH of other cells. The data are provided by the NASA Ames Prognostics Center of Excellence, and are characterised by varying loads which simulate dynamic operating conditions. Two hypothetical scenarios are considered: one assumes that a recent observed capacity measurement is known, the other is based only on the nominal capacity of the cell. It was shown that the degradation behaviour of the batteries under examination is influenced by their historical data, as supported by the low prediction errors achieved (root mean squared errors ranging from 1.2% to 7.22% when considering data up to the battery End of Life). Moreover, we offer a multi-factor perspective where the degree of impact of each different factor to ageing acceleration is analysed. Finally, we compare with a Long Short-Term Memory Neural Network and other works from the literature on the same dataset. We conclude that the MFP regression is effective and competitive with contemporary works, and provides several additional advantages e.g. in terms of interpretability, generalisability, and implementability.

• Lohrmann, Timo; Kvellestad, Anders & Bin, Riccardo De (2022). A modified dividing local Gaussian processes algorithm for theoretical particle physics applications. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. p. 222–227. Full text in Research Archive Show summary

  We explore the use of dividing local Gaussian processes in the context of theoretical particle physics. We adapt an existing algorithm to the specific problem, trading some speed for a better precision. An intensive sensitivity analysis is performed.

• Midtfjord, Alise Danielle; Bin, Riccardo De & Huseby, Arne Bang (2022). A decision support system for safer airplane landings: Predicting runway conditions using XGBoost and explainable AI. Cold Regions Science and Technology. ISSN 0165-232X. 199. doi: 10.1016/j.coldregions.2022.103556. Full text in Research Archive Show summary

  The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classification model to identify slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. The XGBoost models are combined with SHAP approximations to provide a reliable decision support system for airport operators, which can contribute to safer and more economic operations of airport runways. To evaluate the performance of the prediction models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy.

• Hubin, Aliaksandr & Bin, Riccardo De (2022). On a genetically modified mode jumping MCMC approach for multivariate fractional polynomials. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. p. 478–483. Full text in Research Archive Show summary

  In this work, we suggest a framework to fit fractional polynomials based on the Bayesian Generalized Nonlinear Models (BGNLM, Hubin et al, 2021). A version of the Genetically Modified Mode Jumping Markov Chain Monte Carlo (GMJMCMC) algorithm (Hubin et al, 2020) is adopted. Preliminary simulation runs show promising results in terms of identifying the data generation mechanism: The suggested approach uniformly outperforms the exist- ing Bayesian fractional polynomial framework of Saban ́es Bov ́e and Held (2011) both in terms of Power and false discovery rate (FDR). Also, the performance is on par (somewhat better) with that of frequentist fractional polynomials of Royston and Altman (1994). Still, the results indicate that further work on the priors is likely to improve the performance even further.

• Midtfjord, Alise Danielle; Bin, Riccardo De & Huseby, Arne Bang (2021). A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data. arXiv.org. ISSN 2331-8422. Full text in Research Archive Show summary

  The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classification model to identify slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. The XGBoost models are combined with SHAP approximations to provide a reliable decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways. To evaluate the performance of the prediction models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy.

• Moss, Jonas & Bin, Riccardo De (2021). Modelling publication bias and p-hacking. Biometrics. ISSN 0006-341X. doi: 10.1111/biom.13560. Full text in Research Archive Show summary

  Publication bias and p-hacking are two well-known phenomena that strongly affect the scientific literature and cause severe problems in meta-analyses. Due to these phenomena, the assumptions of meta-analyses are seriously violated and the results of the studies cannot be trusted. While publication bias is very often captured well by the weighting function selection model, p-hacking is much harder to model and no definitive solution has been found yet. In this paper, we advocate the selection model approach to model publication bias and propose a mixture model for p-hacking. We derive some properties for these models, and we compare them formally and through simulations. Finally, two real data examples are used to show how the models work in practice.

• Aiken, John Mark; Bin, Riccardo De; Lewandowski, Heather & Caballero, Marcos Daniel (2021). Framework for evaluating statistical models in physics education research. Physical Review Physics Education Research. 17(2). doi: 10.1103/PhysRevPhysEducRes.17.020104. Full text in Research Archive Show summary

  Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large datasets and machine learning techniques. In physics education research (PER), this increased focus has recently been shown through the 2019 Physical Review PER Focused Collection examining quantitative methods in PER. Quantitative PER has provided strong arguments for reforming courses by including interactive engagement, demonstrated that students often move away from scientistlike views due to science education, and has injected robust assessment into the physics classroom via concept inventories. The work presented here examines the impact that machine learning may have on physics education research, presents a framework for the entire process including data management, model evaluation, and results communication, and demonstrates the utility of this framework through the analysis of two types of survey data.

• Belhechmi, Shaima; Bin, Riccardo De; Rotolo, Federico & Michiels, Stefan (2020). Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models. BMC Bioinformatics. 21. doi: 10.1186/s12859-020-03618-y. Full text in Research Archive Show summary

  Background The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not take into account the case of predictors belonging to functional groups; typically, genomic data can be grouped according to biological pathways or to different types of collected data. Another challenge is that the standard lasso penalisation is known to have a high false discovery rate. Results We evaluated different penalizations in a Cox model to select grouped variables in order to further penalize variables that, in addition to having a low effect, belong to a group with a low overall effect; and to favor the selection of variables that, in addition to having a large effect, belong to a group with a large overall effect. We considered the case of prespecified and disjoint groups and proposed diverse weights for the adaptive lasso method. In particular we proposed the product Max Single Wald by Single Wald weighting (MSW*SW) which takes into account the information of the group to which it belongs and of this biomarker. Through simulations, we compared the selection and prediction ability of our approach with the standard lasso, the composite Minimax Concave Penalty (cMCP), the group exponential lasso (gel), the Integrative L1-Penalized Regression with Penalty Factors (IPF-Lasso), and the Sparse Group Lasso (SGL) methods. In addition, we illustrated the methods using gene expression data of 614 breast cancer patients. Conclusions The adaptive lasso with the MSW*SW weighting method incorporates both the information in the grouping structure and the individual variable. It outperformed the competitors by reducing the false discovery rate without severely increasing the false negative rate.

• Fuchs, Mathias; Hornung, Roman; Boulesteix, Anne-Laure & Bin, Riccardo De (2020). On the asymptotic behaviour of the variance estimator of a U-statistic. Journal of Statistical Planning and Inference. ISSN 0378-3758. 209, p. 101–111. doi: 10.1016/j.jspi.2020.03.003. Full text in Research Archive
• Aiken, John Mark; Bin, Riccardo De; Hjorth-Jensen, Morten & Caballero, Marcos Daniel (2020). Predicting time to graduation at a large enrollment American university. PLOS ONE. doi: 10.1371/journal.pone.0242334. Full text in Research Archive Show summary

  The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto’s Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).

• Wallisch, Christine; Dunkler, Daniela; Rauch, Geraldine; Bin, Riccardo De & Heinze, Georg (2020). Selection of variables for multivariable models: opportunities and limitations in quantifying model stability by resampling. Statistics in Medicine. ISSN 0277-6715. 40(2), p. 369–381. doi: 10.1002/sim.8779. Full text in Research Archive Show summary

  Statistical models are often fitted to obtain a concise description of the association of an outcome variable with some covariates. Even if background knowledge is available to guide preselection of covariates, stepwise variable selection is commonly applied to remove irrelevant ones. This practice may introduce additional variability and selection is rarely certain. However, these issues are often ignored and model stability is not questioned. Several resampling‐based measures were proposed to describe model stability, including variable inclusion frequencies (VIFs), model selection frequencies, relative conditional bias (RCB), and root mean squared difference ratio (RMSDR). The latter two were recently proposed to assess bias and variance inflation induced by variable selection. Here, we study the consistency and accuracy of resampling estimates of these measures and the optimal choice of the resampling technique. In particular, we compare subsampling and bootstrapping for assessing stability of linear, logistic, and Cox models obtained by backward elimination in a simulation study. Moreover, we exemplify the estimation and interpretation of all suggested measures in a study on cardiovascular risk. The VIF and the model selection frequency are only consistently estimated in the subsampling approach. By contrast, the bootstrap is advantageous in terms of bias and precision for estimating the RCB as well as the RMSDR. Though, unbiased estimation of the latter quantity requires independence of covariates, which is rarely encountered in practice. Our study stresses the importance of addressing model stability after variable selection and shows how to cope with it.

• Friederich, Pascal; Gomes, Gabriel Dos Passos; Bin, Riccardo De; Aspuru-Guzik, Alán & Balcells, David (2020). Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex. Chemical Science. ISSN 2041-6520. 11(18), p. 4584–4601. doi: 10.1039/d0sc00445f. Full text in Research Archive Show summary

  Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand–ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol−1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol−1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H2-activation barrier were identified.

• Bin, Riccardo De; Boulesteix, Anne-Laure; Benner, Alex; Becker, Natalia & Sauerbrei, Willi (2019). Combining clinical and molecular data in regression prediction models: insights from a simulation study. Briefings in Bioinformatics. ISSN 1467-5463. doi: 10.1093/bib/bbz136. Full text in Research Archive Show summary

  Data integration, i.e. the use of different sources of information for data analysis, is becoming one of the most important topics in modern statistics. Especially in, but not limited to, biomedical applications, a relevant issue is the combination of low-dimensional (e.g. clinical data) and high-dimensional (e.g. molecular data such as gene expressions) data sources in a prediction model. Not only the different characteristics of the data, but also the complex correlation structure within and between the two data sources, pose challenging issues. In this paper, we investigate these issues via simulations, providing some useful insight into strategies to combine low- and high-dimensional data in a regression prediction model. In particular, we focus on the effect of the correlation structure on the results, while accounting for the influence of our specific choices in the design of the simulation study.

• Volkmann, Alexander; Bin, Riccardo De; Sauerbrei, Willi & Boulesteix, Anne-Laure (2019). A plea for taking all available clinical information into account when assessing the predictive value of omics data. BMC Medical Research Methodology. 19, p. 1–15. doi: 10.1186/s12874-019-0802-0. Full text in Research Archive Show summary

  Background: Omics data can be very informative in survival analysis and may improve the prognostic ability of classical models based on clinical risk factors for various diseases, for example breast cancer. Recent research has focused on integrating omics and clinical data, yet has often ignored the need for appropriate model building for clinical variables. Medical literature on classical prognostic scores, as well as biostatistical literature on appropriate model selection strategies for low dimensional (clinical) data, are often ignored in the context of omics research. The goal of this paper is to fill this methodological gap by investigating the added predictive value of gene expression data for models using varying amounts of clinical information. Methods: We analyze two data sets from the field of survival prognosis of breast cancer patients. First, we construct several proportional hazards prediction models using varying amounts of clinical information based on established medical knowledge. These models are then used as a starting point (i.e. included as a clinical offset) for identifying informative gene expression variables using resampling procedures and penalized regression approaches (model based boosting and the LASSO). In order to assess the added predictive value of the gene signatures, measures of prediction accuracy and separation are examined on a validation data set for the clinical models and the models that combine the two sources of information. Results: For one data set, we do not find any substantial added predictive value of the omics data when compared to clinical models. On the second data set, we identify a noticeable added predictive value, however only for scenarios where little or no clinical information is included in the modeling process. We find that including more clinical information can lead to a smaller number of selected omics predictors. Conclusions: New research using omics data should include all available established medical knowledge in order to allow an adequate evaluation of the added predictive value of omics data. Including all relevant clinical information in the analysis might also lead to more parsimonious models. The developed procedure to assess the predictive value of the omics data can be readily applied to other scenarios. Keywords: Data integration, Cox regression, Model building

• Belhechmi, Shaima; Bin, Riccardo De; Michiels, Stefan & Rotolo, Federico (2019). Prise en compte des groupes de biomarqueurs ou des voies biologiques dans les modèles de Cox pénalisés de haute dimension. Revue d'épidémiologie et de santé publique. ISSN 0398-7620. 67. Full text in Research Archive
• Gruting, Isabelle van; Kluivers, Kirsten B; Sultan, Abdul H.; Bin, Riccardo De; Stankiewicz, Aleksandra & Blake, H. [Show all 7 contributors for this article] (2018). Does 4D transperineal ultrasound have additional value over 2D transperineal ultrasound for diagnosing posterior pelvic floor disorders in women with obstructed defaecation syndrome? Ultrasound in Obstetrics and Gynecology. ISSN 0960-7692. 52, p. 784–791. Full text in Research Archive
• Bin, Riccardo De & Sauerbrei, Willi (2018). Handling co-dependence issues in resampling-based variable selection procedures: a simulation study. Journal of Statistical Computation and Simulation. ISSN 0094-9655. 88(1), p. 28–55. doi: 10.1080/00949655.2017.1378654. Full text in Research Archive Show summary

  If a number of candidate variables are available, variable selection is a key task aiming to identify those candidates which influence the outcome of interest. Methods as backward elimination, forward selection, etc. are often implemented, despite their drawbacks. One of these drawbacks is the instability of their results with respect to small perturbations in the data. To handle this issue, resampling-based procedures have been introduced; using a resampling technique, e.g. bootstrap, these procedures generate several pseudo-samples that are used to compute the inclusion frequency of each variable, i.e. the proportion of pseudo-samples in which the variable is selected. Based on the inclusion frequencies, it is possible to discriminate between relevant and irrelevant variables. These procedures may fail in case of correlated variables. To deal with this issue, two procedures based on 2×2 tables of inclusion frequencies have been developed in the literature. In this paper we analyse the behaviours of these two procedures and the role of their tuning parameters in an extensive simulation study.

• Bin, Riccardo De (2017). Overview of Topics Related to Model Selection for Regression. Trends in Mathematics. ISSN 2297-0215. 7, p. 77–82. doi: 10.1007/978-3-319-55639-0_13. Full text in Research Archive
• Seibold, Heidi; Bernau, Christoph; Boulesteix, Anne-Laure & Bin, Riccardo De (2017). On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. Computational statistics (Zeitschrift). ISSN 0943-4062. p. 1195–1215. doi: 10.1007/s00180-017-0773-8. Full text in Research Archive Show summary

  In biomedical research, boosting-based regression approaches have gained much attention in the last decade. Their intrinsic variable selection procedure and ability to shrink the estimates of the regression coefficients toward 0 make these techniques appropriate to fit prediction models in the case of high-dimensional data, e.g. gene expressions. Their prediction performance, however, highly depends on specific tuning parameters, in particular on the number of boosting iterations to perform. This crucial parameter is usually selected via cross-validation. The cross-validation procedure may highly depend on a completely random component, namely the considered fold partition. We empirically study how much this randomness affects the results of the boosting techniques, in terms of selected predictors and prediction ability of the related models. We use four publicly available data sets related to four different diseases. In these studies, the goal is to predict survival end-points when a large number of continuous candidate predictors are available. We focus on two well known boosting approaches implemented in the R-packages CoxBoost and mboost, assuming the validity of the proportional hazards assumption and the linearity of the effects of the predictors. We show that the variability in selected predictors and prediction ability of the model is reduced by averaging over several repetitions of cross-validation in the selection of the tuning parameters. The final version of this research has been published in Computational Statistics. © 2017 Springer Verlag

• Boulesteix, Anne-Laure; Bin, Riccardo De; Jiang, Xiaoyu & Fuchs, Mathias (2017). IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. Computational & Mathematical Methods in Medicine. ISSN 1748-670X. 2017. doi: 10.1155/2017/7691937. Full text in Research Archive Show summary

  As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.

• Gruting, Isabelle van; Stankiewicz, Aleksandra; Kluivers, Kirsten B; Bin, Riccardo De; Blake, Helena & Sultan, Abdul H. [Show all 7 contributors for this article] (2017). Accuracy of Four Imaging Techniques for Diagnosis of Posterior Pelvic Floor Disorders. Obstetrics and Gynecology. ISSN 0029-7844. 130(5), p. 1017–1024. doi: 10.1097/AOG.0000000000002245. Full text in Research Archive
• Bin, Riccardo De; Boulesteix, Anne-Laure & Sauerbrei, Willi (2017). Detection of influential points as a byproduct of resampling-based variable selection procedures. Computational Statistics & Data Analysis. ISSN 0167-9473. 116, p. 19–31. doi: 10.1016/j.csda.2017.07.001. Full text in Research Archive Show summary

  Influential points can cause severe problems when deriving a multivariable regression model. A novel approach to check for such points is proposed, based on the variable inclusion matrix, a simple way to summarize results from resampling-based variable selection procedures. The variable inclusion matrix reports whether a variable (column) is included in a regression model fitted on a pseudo-sample (row) generated from the original data (e.g., bootstrap sample or subsample). It is used to study the variable selection stability, to derive weights for model averaged predictors and in others investigations. Concentrating on variable selection, it also allows understanding whether the presence of a specific observation has an influence on the selection of a variable. From the variable inclusion matrix, indeed, the inclusion frequency (I-frequency) of each variable can be computed only in the pseudo-samples (i.e., rows) which contain the specific observation. When the procedure is repeated for each observation, it is possible to check for influential points through the distribution of the I-frequencies, visualized in a boxplot, or through a Grubbs’ test. Outlying values in the former case and significant results in the latter point to observations having an influence on the selection of a specific variable and therefore on the finally selected model. This novel approach is illustrated in two real data examples.

• Bin, Riccardo De (2016). Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost. Computational statistics (Zeitschrift). ISSN 0943-4062. 31, p. 513–531. doi: 10.1007/s00180-015-0642-2. Full text in Research Archive
• Bin, Riccardo De (2016). A note on the equivalence between conditional and random-effects likelihoods in exponential families. Statistics and Probability Letters. ISSN 0167-7152. 110, p. 34–38. doi: 10.1016/j.spl.2015.12.002. Full text in Research Archive
• Bin, Riccardo De; Janitza, Silke; Sauerbrei, Willi & Boulesteix, Anne-Laure (2016). Subsampling versus bootstrap in resampling-based model selection for multivariable regression. Biometrics. ISSN 0006-341X. 72(1), p. 272–280. doi: 10.1111/biom.12381. Full text in Research Archive

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• Bin, Riccardo De; Salucci, Clara Bertinelli & Hubin, Aliaksandr (2026). Proceedings of the 40th International Workshop on Statistical Modelling 28.06 – 03.07.2026, Oslo, Norway. International Workshop on Statistical Modelling. ISBN 9788255325000. 655 p. Full text in Research Archive

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• Salucci, Clara Bertinelli; Kvellestad, Anders & Bin, Riccardo De (2025). Likelihood Ratio Tests with Parameters on the Boundary: New Insights and an Application to Particle Physics. Full text in Research Archive Show summary

  In order to perform likelihood ratio tests for model comparison, we often rely on Wilks' theorem, i.e. we assume that the related test statistic is chi-squared distributed with degrees of freedom corresponding to the number of estimated parameters of interest. This result relies on a set of sufficient regularity conditions - including smoothness, identifiability, and the assumption that the true parameter lies in the interior of the parameter space. In many applied problems, these assumptions are not satisfied. This talk focuses on violations of the latter condition, specifically on boundary issues affecting one or more parameters of interest, possibly in the presence of nuisance parameters. A boundary violation occurs when a parameter is exactly on the boundary of the parameter space under the null hypothesis. In such cases, the standard chi-squared approximation for the likelihood ratio statistic no longer holds. We discuss situations in which these approximations may fail, adopting as motivating example the search for new particles in high-energy physics. Testing for the presence of a new signal from undiscovered particles typically involves a null hypothesis in which the signal strength is zero: a parameter on the boundary. The presence of additional nuisance parameters -- for instance, related to background modelling and strength or detector response -- may further complicate the derivation of the asymptotic behaviour of the test statistic. Recognising and properly accounting for these deviations is essential for drawing reliable statistical conclusions, both in high-energy physics and, in general, in any application where constrained parameters are involved. We review adjusted asymptotic distributions proposed in the literature, including chi-squared mixtures derived from geometric and algebraic arguments, and extend them to handle our case of study.

• Sauerbrei, Willi; Ambrogi, Federico; Bin, Riccardo De; Boulesteix, Anne-Laure; Goetghebeur, Els & Huebner, Marianne (2025). Commentary: Regression Models—Efforts Are Required to Improve Statistical Practice and Teaching. Statistics in Medicine. ISSN 0277-6715. doi: 10.1002/sim.10341. Full text in Research Archive
• Bin, Riccardo De (2025). A boosting model for survival analysis with dependent censoring. Full text in Research Archive
• Bin, Riccardo De; Hornung, Roman; Gandin, Ilaria; Lusa, Lara & Michiels, Stefan (2024). A gentle introduction to tuning parameters: their role, their importance, and how to choose them. Full text in Research Archive
• Bin, Riccardo De (2024). Boosting first-hitting-time models for time-to-event analysis. Full text in Research Archive
• Bin, Riccardo De & Heinze, Georg (2023). GMJMCMC for Bayesian Fractional Polynomials. Full text in Research Archive
• Meggio, Claudio; Balcells, David; Pensar, Johan & Bin, Riccardo De (2023). Path Boosting, a statistical learning method for chemistry applications. Full text in Research Archive
• Meggio, Claudio; Balcells, David; Bin, Riccardo De & Pensar, Johan (2023). Adapting Gradient Booting methods to graph inputs. Full text in Research Archive
• Bin, Riccardo De (2022). A boosting first hitting time model for survival analysis in high-dimensional settings. Full text in Research Archive
• Bin, Riccardo De (2022). First-hitting-time models for high-dimensional data: A statistical boosting approach. Full text in Research Archive
• Bin, Riccardo De (2022). A boosting model for survival analysis with dependent censoring. Full text in Research Archive
• Song, You; Lillicrap, Adam David; Esguerra, Camila Vicencio; Villeneuve, Daniel L.; Ng, Daniel & Rundberget, Jan Thomas [Show all 15 contributors for this article] (2022). Quantitative Adverse Outcome Pathways Assisted Next Generation Risk Assessment of Mitochondrial Toxicants. Full text in Research Archive
• Hubin, Aliaksandr & Bin, Riccardo De (2022). On a genetically modified mode jumping MCMC approach for multivariate fractional polynomials. Full text in Research Archive
• Hubin, Aliaksandr & Bin, Riccardo De (2022). On a genetically modified mode jumping MCMC approach for multivariate fractional polynomials. Full text in Research Archive
• Bin, Riccardo De (2021). Topic group 9 ‘High-Dimensional Data”: updates and plans. Full text in Research Archive
• Bin, Riccardo De (2021). Once upon a time... boosting: How some people betting on an ox are helping us to get right cancer treatments or have safe plane landings. Full text in Research Archive
• Cao, Yang; Moe, Jannicke; Bin, Riccardo De; Tollefsen, Knut-Erik & Song, You (2021). Piecewise structural equation modeling aided construction of a quantitative adverse outcome pathway network. Full text in Research Archive
• Bin, Riccardo De (2021). Analysis of high-dimensional data: Opportunities, challenges and goals. Full text in Research Archive
• Song, You; Villeneuve, Daniel L.; Moe, Jannicke; Cao, Yang; Hultman, Maria Thérése & Christou, Maria [Show all 21 contributors for this article] (2021). Development of quantitative Adverse Outcome Pathways to facilitate next generation risk assessment of mitochondrial toxicants. Full text in Research Archive
• Song, You; Villeneuve, Daniel; Moe, Jannicke; Cao, Yang; Hultman, Maria Thérése & Christou, Maria [Show all 21 contributors for this article] (2021). Development of quantitative Adverse Outcome Pathways to facilitate next generation risk assessment of mitochondrial toxicants. Full text in Research Archive
• Bin, Riccardo De (2021). On the asymptotic behaviour of the variance estimator of a U -statistic. Full text in Research Archive
• Bin, Riccardo De (2021). Modelling publication bias and p-hacking. Full text in Research Archive
• Bin, Riccardo De (2021). On the asymptotic behaviour of the variance estimator of a U -statistic. Full text in Research Archive
• Bin, Riccardo De (2021). “Generalization” from a statistical point of view. Full text in Research Archive
• Moe, Jannicke; Cao, Yang; Bin, Riccardo De; Tollefsen, Knut-Erik & Song, You (2021). Quantification of an AOP network for UV radiation by statstical and causal probabilistic modelling. Full text in Research Archive
• Bin, Riccardo De (2020). A brief overview on topics related to Statistical Learning. Full text in Research Archive
• Bin, Riccardo De (2020). Statistical Learning. Full text in Research Archive
• Song, You; Hultman, Maria Thérése; Moe, Jannicke; Petersen, Karina; Rundberget, Jan Thomas & Xie, Li [Show all 18 contributors for this article] (2020). The RiskAOP project - Quantitative Adverse Outcome Pathway assisted risk assessment of mitochondrial toxicants. Full text in Research Archive
• Bin, Riccardo De (2020). Influence of single observations on the choice of the penalty parameter in ridge regression. Full text in Research Archive
• Bin, Riccardo De (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures. Full text in Research Archive
• Bin, Riccardo De (2019). Statistics meets machine learning: the example of statistical boosting. Full text in Research Archive
• Bin, Riccardo De (2019). Research group “Statistics and Data Science” and my research interests. Full text in Research Archive
• Bin, Riccardo De (2019). Strategies to handle mandatory covariates using model- and likelihood-based boosting. Full text in Research Archive
• Bin, Riccardo De (2019). Strategies to handle mandatory covariates using model- and likelihood-based boosting. Full text in Research Archive
• Bin, Riccardo De (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures. Full text in Research Archive
• Bin, Riccardo De (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures. Full text in Research Archive
• Bin, Riccardo De (2019). Influence of single observations on the choice of the penalty parameter in ridge regression. Full text in Research Archive
• Bin, Riccardo De (2019). First-hitting-time models for high-dimensional data: A statistical boosting approach. Full text in Research Archive
• Bin, Riccardo De (2018). Strategies to derive combined prediction models using both clinical predictors and high-throughput molecular data. Full text in Research Archive
• Bin, Riccardo De (2018). On the equivalence between conditional and random-effects likelihoods in exponential families. Full text in Research Archive
• Bin, Riccardo De (2018). Strategies to derive combined prediction models using both clinical predictors and high-throughput molecular data. Full text in Research Archive
• Fuchs, Mathias; Hornung, Roman; Boulesteix, Anne-Laure & Bin, Riccardo De (2018). An asymptotic test for the equality of error rates based on variance estimation of complete subsampling. Full text in Research Archive
• Bin, Riccardo De (2018). Integrated likelihoods in the context of distributed computing. Full text in Research Archive
• Bin, Riccardo De (2018). Meta-analysis and selection for significance. Full text in Research Archive
• Bin, Riccardo De (2018). Combining low-dimensional clinical and high-dimensional molecular data in a survival prediction model. Full text in Research Archive
• Bin, Riccardo De (2018). Strategies to derive combined prediction models using both clinical predictors and high-throughput molecular data. Full text in Research Archive
• Bin, Riccardo De (2018). Strategies to handle mandatory covariates using model- and likelihood-based boosting. Full text in Research Archive
• Bin, Riccardo De (2018). On combining clinical and omics data in regression prediction models. Full text in Research Archive
• Bin, Riccardo De (2017). Extensions of threshold-hitting models to higher dimension. Full text in Research Archive
• Bin, Riccardo De (2017). Integrated likelihoods in the presence of many nuisance parameters. Full text in Research Archive
• Bin, Riccardo De (2017). Strategies to handle mandatory covariates using model- and likelihood-based boosting. Full text in Research Archive
• Bin, Riccardo De (2017). Strategies to handle mandatory covariates using model- and likelihood-based boosting. Full text in Research Archive
• Bin, Riccardo De (2017). Strategies to handle mandatory covariates using model- and likelihood-based boosting. Full text in Research Archive
• Bin, Riccardo De (2017). Integrated likelihoods in the presence of many nuisance parameters. Full text in Research Archive
• Bin, Riccardo De (2017). Detection of influential points as a byproduct of resampling-based variable selection procedures. Full text in Research Archive
• Bin, Riccardo De (2016). Referee Report For: Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration [version 1; referees: 2 approved with reservations]. F1000 Research. 5, p. 2671–2671. doi: 10.5256/f1000research.9340.r18283. Full text in Research Archive

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