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Ongoing

Time and place: , Quality Hotel Fredrikstad

University of Oslo and University of Gothenburg invite to an informal workshop within machine learning with a focus on statistical aspects related to Machine Learning.

Upcoming

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

his talk discusses a nonparametric inference framework for occupation time curves derived from wearable device data. Such curves provide the total time a subject maintains activity above a given level as a function of that level. Taking advantage of the monotonicity and smoothness properties of these curves, we develop a likelihood ratio approach to construct confidence bands for mean occupation time curves.  An extension to fitting concurrent functional regression models is also developed. Application to wearable device data from an ongoing study of an experimental gene therapy for mitochondrial DNA depletion syndrome will be discussed. Based on joint work with Hsin-Wen Chang (Academia Sinica).

 

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

Structural equation models are simultaneous equation regression models, whose variables are latent, and measured via a confirmatory factor model (that is, with measurement error and repeated measurements). When the functional form of the simultaneous equation system is unknown, it has previously been observed in simulations that factor scores inputted into non-parametric regression methods approximate the true functional form. Factor scores estimate the latent variables (per person), and several types exist. We provide a theoretical (though population-based) analysis of this procedure, and provide assumptions under which it is theoretically justified in using Bartlett factor scores, which are simple linear transformations of the data. In simulations, we compare this suggestion to an already available though understudied non-linear and computationally heavy procedure, and observe that the simple Bartlett approach appears to work better.

Previous

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor and zoom: https://uio.zoom.us/j/68412228703?pwd=Y2FFZDlCSzBZbDZ4Rkw0S2NQWHpTQT09

The climatic ocean wave spectrum serves as a pivotal tool in comprehending the long-term characteristics and variations of wave patterns across different regions of the world's oceans. The presentation explores the methodologies employed to derive wave spectra from observational data. Basically, consists of a statistical approach that provides a quantitative understanding of the variability and extremes of wave conditions. In essence, an ocean wave spectrum is a representation of the distribution of energy among different wave frequencies and wavelengths. So, engineers rely on this valuable information to mitigate risks and design solutions that can withstand the dynamic forces of ocean waves. However, it is necessary to present such information in a robust and practical mode to better comprehend the variations. In this way, a robust and resistant approach will be presented to define such variabilities, thus reducing uncertainties and representing the climatic wave spectrum in a compact and informative way.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

Our project partner Statkraft owns and operates several hydropower plants in Brazil and requires information about the future potential for hydropower production in this region. To provide inflow projections for the next several decades, we use climate model output in combination with a regression model that links meteorological variables such as precipitation and temperature to inflow over various catchments in the region. The relatively short time period for which observation data are available raises concerns about overfitting. We therefore explore an alternative model fitting approach that retains the original, easily interpretable regression model but estimates the regression coefficients within an artificial neural network (ANN) framework which permits spatial and temporal regularization and thus prevents overfitting. We show some examples of the inflow projections obtained with that methodology and discuss some caveats and limitations.

Time and place: , Auditorium 1, Vilhelm Bjerknes hus, Blindern

We invite you to a two day seminar celebrating Nils Lid Hjort's significant and extensive contributions in statistics.

Time and place: , Tøyen Hovedgård

On November 21-23 the Integreat team and partners convened for the first-ever kick-off meeting at the historic Tøyen Hovedgård in Oslo. The event marked a crucial milestone for the Integreat community and served as an opportunity to articulate common goals, define the purpose of upcoming projects, and facilitate team building.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
This paper considers hypothesis testing in semiparametric models which may be non – regular for certain values of a (potentially infinite dimensional) nuisance parameter. In such models no (locally) regular estimator of the parameter of interest exists. The situation for testing is somewhat different: I establish that C(α) – style test statistics achieve their limiting distributions in a (locally) regular manner under mild conditions, leading to tests with correct size in situations where standard tests fail to control size. Additionally, I characterise the appropriate limit experiment in which to study local (asymptotic) optimality of tests in the case where the efficient information matrix is singular. This permits the generalisation of classical power bounds to the non – regular case. I provide appropriate statements of these bounds and give conditions under which they are attained by the proposed C(α) – style tests. Three examples are worked out in detail.
Time and place: , Erling Svedrups plass and Zoom https://uio.zoom.us/j/64912028556?pwd=QmJpa1ZPS0hBNTFZUDhzWDlaMmJKQT09

Traditional quantile estimators are not well-suited for data streams because the memory and computational time increase with the volume of data received from the stream. Incremental quantile estimators refer to a class of methods designed to maintain quantile estimates for data streams. These methods operate by making small updates to the estimate every time a new observation is received from the stream. In this presentation, I will introduce some of the incremental quantile estimators we have developed.

Time and place: , Erling Svedrups plass and Zoom https://uio.zoom.us/j/66849037258?pwd=NkNiR0lkbm5VK0VyMytVZW4vV0hNQT09
Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

This talk will focus on recent work about the sequential detection of anomalies within partially observed functional data, motivated by a problem encountered by an industrial collaborator. Classical sequential changepoint detection approaches look for changes in the parameters, or structure, of a data sequence and are not equipped to handle the complex non-stationarity and dependency structure of functional data. Conversely, existing functional data approaches require the full observation of the curve before anomaly detection can take place. We propose a new method, FAST, that performs sequential detection of anomalies in partially observed functional data. This talk will introduce the approach, and some associated theoretical results, and highlight its application on telecommunications data.

This is joint work with Idris Eckley and Lawrence Bardwell.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets.  Given the representation of the probability model in extended nonlinear state-space form, efficient simulation-based Bayesian inference is proposed using parallel dynamic clustering as well as nonlinear filtering, implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual US stock returns of daily observations over a period that includes the Covid-19 crisis period.  Evidence on dynamic cluster composition, weight patterns and model set incompleteness gives valuable signals for improved modelling. This enables higher predictive accuracy and better assessment of uncertainty and risk for investment fund management.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

Online changepoint detection algorithms based on likelihood-ratio tests have excellent statistical properties. However, a simple exact online implementation is computationally infeasible as, at time T, it involves considering O(T) possible locations for the change. To improve on this, we use functional pruning ideas to reduce the set of changepoint locations that need to be stored at time T to approximately log T. Furthermore, we show how we need only maximise the likelihood-ratio test statistic over a small subset of these possible locations. Empirical results show that the resulting exact online algorithm, which can detect changes under a wide range of models, has a constant-per-iteration cost on average. We consider applications of this algorithm in the context of detecting increases in radiation count that represent astronomical or nuclear events of interest.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

This talk will introduce a recent suite of research focussed on the statistical detection of anomalous structure in online data settings. The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Whilst there has been substantial work aimed at making statistical analyses robust to outliers, or point anomalies, there has been much less work on detecting anomalous segments, or collective anomalies, particularly in those settings where point anomalies might also occur. This is the challenge we seek to address, demonstrating theoretical results in both the offline and online settings as well as introducing some applied case studies.

Time and place: , Niels Henrik Abels hus, 8th floor

Douglas Wiens (Department of Mathematics and Statistical Sciences, University of Alberta, CAN) will give a talk on Wednesday April 19th at 14:15 in the Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

Variable selection methods based on L0 penalties have excellent theoretical properties to select sparse models in a high-dimensional setting. There exist modifications of BIC which either control the family wise error rate (mBIC) or the false discovery rate (mBIC2) in terms of which regressors are selected to enter a model. However, the minimization of L0 penalties comprises a mixed integer problem which is known to be NP hard and therefore becomes computationally challenging with increasing numbers of regressor variables. This is one reason why alternatives like the LASSO have become so popular, which involve convex optimization problems which are easier to solve. The last few years have seen some real progress in developing new algorithms to minimize  L0 penalties. We will compare the performance of these algorithms in terms of minimizing L0 based selection criteria.
Simulation studies covering a wide range of scenarios which are inspired by genetic association studies are used to compare the values of selection criteria obtained with different algorithms. Additionally some statistical characteristics of the selected models and the runtime of algorithms are compared.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor

Estimates of environmental extremes are needed for a multitude of applications. For example, buildings, roads, bridges and dams must be designed to withstand extreme precipitation and flooding events of a certain size. Obtaining such estimates requires a combination of statistical theory and environmental process understanding to overcome data deficiencies: data on extremes are by definition sparse and regulations often require estimates for events that have yet to be observed. We will present approaches to obtain consistent estimates across spatial locations and accumulation periods, and discuss a few open questions on this topic. 

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
Time and place: , Abels utsikt, 12th floor of Niels Henrik Abels hus

On the occasion of Jørund Gåsemyr retiring earlier this year, we invite you to a half-day seminar celebrating his contributions to statistics over many years.

Time and place: , Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor
Time and place: , Klækken hotell

We are delighted to finally again be able to invite you to the Klækken seminar, October 11 – October 12, 2022.  Klækken seminar is open for young researchers in temporary positions within statistics/biostatistics/bioinformatics at OCBE (UiO and OUS), Institute of Mathematics (UiO), BiAs (NMBU) and RealTek (NMBU).  It is meant as an arena to get to know each other across the institutes and to practice your presentations skills in front of a friendly and harmless audience.