ZüKoSt: Seminar on Applied Statistics

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Autumn Semester 2018

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21 September 2018
15:15-16:00

Karsten Borgwardt
ETH Zürich

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ZüKoSt Zürcher Kolloquium über Statistik

Title	CANCELLED!!
Speaker, Affiliation	Karsten Borgwardt, ETH Zürich
Date, Time	21 September 2018, 15:15-16:00
Location	HG G 19.1
Abstract	One key challenge in Machine Learning in Medicine is Association Mapping: to link genetic properties of patients to disease risk, progression and therapy success, in order to then exploit this knowledge for improved diagnosis, prognosis and treatment. Disappointingly, for most complex diseases, current feature selection methods have failed to discover strong associations. One possible explanation is that the vast majority of current methods ignores disease-related interactions between genetic properties - combinations of genome variants that jointly affect a disease. The difficulty in exploring these interactions through Combinatorial Association Mapping stems from the combinatorial explosion of the candidate space, which grows exponentially with the number of interacting loci. This leads both to an enormous computational efficiency problem and a severe multiple testing problem. Ignoring this multiple testing problem may lead to millions of false positive associations; accounting for it may lead to a complete loss of statistical power. For this reason, statistically sound and efficient Combinatorial Association Mapping was long deemed an unsolvable problem. In this talk, we will describe our recent progress in solving this problem of Combinatorial Association Mapping, and we will give an outlook on how our new association mapping algorithms will be applied in the “Personalized Swiss Sepsis Study”, as part of the Swiss Personalized Health Network (SPHN).

CANCELLED!!read_more

HG G 19.1

25 September 2018
15:15-16:00

Douglas Bates
University of Wisconsin - Madison

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ZüKoSt Zürcher Kolloquium über Statistik

Title	Recent Computational Advances for Mixed-effects Modeling
Speaker, Affiliation	Douglas Bates, University of Wisconsin - Madison
Date, Time	25 September 2018, 15:15-16:00
Location	HG G 19.1
Abstract	The lme4 package for R is widely used (google scholar claims more than 10,000 citations of our 2015 J. Stat. Soft. paper on it) but many of its users still encounter convergence problems or long delays in fitting complex models to large data sets. Several years ago I became interested in using the Julia programming language (julialang.org) to reimplement and improve the algorithms in lme4. The good news is that this project has been, I think, successful in that the MixedModels package provides fast and reliable fitting of both linear mixed-effects models and generalized linear mixed-effects models. However, not everyone is willing to switch to a new programming language to be able to take advantage of one package that may only be a small part of their usage. Julia does not yet have the scope and level of expertise for data analysis, manipulation and visualization that R does. It becomes important to provide the ability to communicate between the languages and, in particular, to exchange data between them. I will discuss some of the capabilities in Julia that make the development of the MixedModels package feasible and some of the mechanisms for communications between the languages.

Recent Computational Advances for Mixed-effects Modelingread_more

HG G 19.1

11 January 2019
15:15-16:00

Philippe Naveau
Laboratoire des Sciences du Climat et l'Environnement (LSCE) CNRS

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ZüKoSt Zürcher Kolloquium über Statistik

Title	Stochastic rainfall generator based on extreme value theory
Speaker, Affiliation	Philippe Naveau, Laboratoire des Sciences du Climat et l'Environnement (LSCE) CNRS
Date, Time	11 January 2019, 15:15-16:00
Location	HG G 19.2
Abstract	The first topic of this talk is to model the marginal distribution of rainfall data, extremes included. Precipitation amounts at daily or hourly scales are skewed to the right and heavy rainfall is poorly modeled by a simple gamma distribution. An important, yet challenging topic in hydrometeorology is to find a probability distribution that is able to model well low, moderate and heavy rainfall. In this context, another important aspect of your work is to completely bypass the threshold selection step, the latter being classically used to in Extreme Value Theory to deal with heavy rainfall. To address this issue, I will discuss different approaches and, in particular, I will emphasise a recent semiparametric distribution suitable for modeling the entire-range of rainfall amount. This joint work with P. Tencaliec, A.C. Favre and C. Prieur and it extends the article of Naveau P, Huser R, Ribereau P, Hannart A, (2016, WRR). In a second step, I will focus on how to couple different sources of data to accurately simulate the multivariate dependence structure among extremes rainfall. This is a joint with Marco Oesting. A convenient starting point to model the dependence among block maxima from environmental datasets is the class of max-stable processes. A typical max-stable can be represented by a max-linear combination that merge independent copies of a hidden stochastic process weighted by a Poisson point process. In practice, other levels of complexity emerge. For our example at hand, the spatial structure of heavy rainfall may neither be anisotropic nor stationary in space. By combining different data sources, we propose different types of data driven max-stables processes. They have the advantages to be parsimonious in parameters, easy to simulate and physically based (i.e. capable of incorporating nugget effects and reproducing spatial non-stationarity). We also compare our new method with classical approaches such as Brown-Resnick types. All our multivariate models are based on the recent work of Oesting (2017).

Stochastic rainfall generator based on extreme value theoryread_more

HG G 19.2

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