clarabe

• Reiersrud, Hedvig Borgen; Salucci, Clara Bertinelli; Kvellestad, Anders & Bin, Riccardo De (2026). Confidence Curve Combination as an Alternative to Wilks in High-Energy Physics. I Bin, Riccardo De; Salucci, Clara Bertinelli & Hubin, Aliaksandr (Red.), Proceedings of the 40th International Workshop on Statistical Modelling 28.06 – 03.07.2026, Oslo, Norway. International Workshop on Statistical Modelling. ISSN 9788255325000. s. 174–179. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Salucci, Clara Bertinelli; Crujeiras, Rosa M.; Bin, Riccardo De; Durbán, Maria; Gijbels, Irène & Kneib, Thomas [Vis alle 8 forfattere av denne artikkelen] (2026). Discussion on: Shape constrained additive smooth models by Simon N. Wood. Statistical Modelling. ISSN 1471-082X. doi: 10.1177/1471082x261447615. Fulltekst i vitenarkiv
• 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. Fulltekst i vitenarkiv Vis sammendrag

  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, s. 300–339. doi: 10.1007/s10985-025-09648-z. Fulltekst i vitenarkiv
• Vanem, Erik; Liang, Qin; Ferreira, Carla; Agrell, Christian; Karandikar, Nikita & Wang, Shuai [Vis alle 14 forfattere av denne artikkelen] (2023). DATA-DRIVEN APPROACHES TO DIAGNOSTICS AND STATE OF HEALTH MONITORING OF MARITIME BATTERY SYSTEMS, PROCEEDINGS OF THE ANNUAL CONFERENCE OF THE PHM SOCIETY 2023. PHM society. ISSN 9781936263295. doi: 10.36001/phmconf.2023.v15i1.3437. Fulltekst i vitenarkiv Vis sammendrag

  Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is important to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) can be verified by independent tests. However, this paper addresses data-driven approaches to state of health monitoring of maritime battery systems based on operational sensor data. Results from various approaches to sensor-based, data-driven degradation monitoring of maritime battery systems will be presented, and advantages and challenges with the different methods will be discussed. The different approaches include cumulative degradation models and snapshot models. Some of the models need to be trained, whereas others need no prior training. Moreover, some of the methods only rely on measured data, such as current, voltage and temperature, whereas others rely on derived quantities such as state of charge (SOC). Models include simple statistical models and more complicated machine learning techniques. Different datasets have been used in order to explore the various methods, including public datasets, data from laboratory tests and operational data from ships in actual operation. Lessons learned from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.

• 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. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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. I Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Red.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. s. 85–90. Fulltekst i vitenarkiv Vis sammendrag

  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. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Vanem, Erik; Salucci, Clara Bertinelli; Bakdi, Azzeddine & Alnes, Øystein Åsheim (2021). Data-driven state of health modelling—A review of state of the art and reflections on applications for maritime battery systems. Journal of Energy Storage. ISSN 2352-152X. 43, s. 1–27. doi: 10.1016/j.est.2021.103158. Fulltekst i vitenarkiv Vis sammendrag

  Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and manoeuvring is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests — annual capacity tests. However, this paper discusses data-driven state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

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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 s. Fulltekst i vitenarkiv

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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. Fulltekst i vitenarkiv Vis sammendrag

  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.

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