ZüKoSt: Seminar on Applied Statistics

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27 January 2016
16:15-17:00

Simon Wood
University of Bath

Event Details

Title	Smoothing parameter and model selection for general smooth models
Speaker, Affiliation	Simon Wood, University of Bath
Date, Time	27 January 2016, 16:15-17:00
Location	HG G 19.1
Abstract	This talk discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. The approach allows smoothing parameter uncertainty to be quantified, suggesting a fix for a well known problem with AIC for such models. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for non-exponential family responses (for example beta, ordered categorical, scaled t distribution, negative binomial and Tweedie distributions), generalized additive models for location scale and shape (for example two stage zero inflation models, and Gaussian location-scale models), Cox proportional hazards models and multivariate additive models. The framework reduces the implementation of new model classes to the coding of some standard derivatives of the log likelihood.

Smoothing parameter and model selection for general smooth modelsread_more

HG G 19.1

7 April 2016
16:15-17:00

Christoph Buser
AXA Winterthur

Event Details

Title	Analyse von Telematik-Daten
Speaker, Affiliation	Christoph Buser, AXA Winterthur
Date, Time	7 April 2016, 16:15-17:00
Location	HG G 19.1
Abstract	In den vergangenen 20 Jahren seit der Deregulierung des Schweizer Motorfahrzeug-Versicherungsmarkt sind die Tarifstrukturen immer komplexer geworden. Es werden unterschiedliche Kriterien in verschiedenen Ausprägungen und Stärke bei dem einzelnen Versicherer verwendet. Die statistischen Methoden auf der anderen Seite sind weitgehend dieselben, die Verallgemeinerten Linearen Modelle haben sich im Versicherungsmarkt durchgesetzt. Mit Telematik-Daten kommt eine neue Herausforderung auf die Versicherungsindustrie zu. Einerseits bringt der Ansatz eine individuelle Beurteilung des Fahrverhaltens in Kombination mit der Solidarität innerhalb einer Risikogruppe mit sich. Andererseits sind das Datenvolumen und damit auch die verwendeten statistischen Methoden grundlegend anders als bei klassischer Risikobeurteilung. In der Präsentation werden anhand der Telematik Daten der AXA Winterthur die Herausforderungen bei der Aufbereitung und Plausibilisierung der Rohdaten und deren Darstellung gezeigt, sowie die Entwicklung von Algorithmen zur Zusammenführung der Daten mit Karteninformationen diskutiert. Anhand deskriptiver Analysen werden Muster, Gemeinsamkeiten oder Unterschiede gesucht, welche zur Bildung von Hypothesen dienen. In ersten Schadenmodellen werden diese untersucht.

Analyse von Telematik-Datenread_more

27 April 2016
16:15-17:00

Martyn Plummer
International Agency for Research on Cancer, Lyon, France

Event Details

Title	IS CANCELLED!!!!
Speaker, Affiliation	Martyn Plummer, International Agency for Research on Cancer, Lyon, France
Date, Time	27 April 2016, 16:15-17:00
Location	HG G 19.1

IS CANCELLED!!!!

11 May 2016
16:15-17:00

Andrea Riebler
NTNU, Norway

Event Details

Title	Bayesian hierarchical models for routine use: What do we need?
Speaker, Affiliation	Andrea Riebler, NTNU, Norway
Date, Time	11 May 2016, 16:15-17:00
Location	HG G 19.1
Abstract	Bayesian hierarchical models are formulated to handle complex data dependencies but also to allow the user to include prior or expert knowledge at different model stages. However, the challenge in understanding and controlling all model components, as well as the lack of easy-to-use software may hinder practitioners to use Bayesian hierarchical models. In this talk we present two case studies where we try to address the aforementioned obstacles. The first case study concerns Bayesian disease mapping. We propose to re-think the common Besag-York-Mollie model to facilitate prior definitions and to obtain a better model understanding. The second case study concerns bivariate meta-analysis of diagnostic test studies, where the inclusion of prior knowledge has proven to be beneficial as data are often sparse. To intuitively incorporate available prior knowledge in the last hierarchy level, we show how to use penalised complexity priors. The methodology is implemented in the R-package meta4diag which provides an interactive graphical user interface offering full functionality without requiring any R programming.

Bayesian hierarchical models for routine use: What do we need?read_more

25 May 2016
16:15-17:00

Stephanie Kovalchik
RAND Corporation, USA

Event Details

Title	The Past, Present, and Future of Prediction in Professional Tennis
Speaker, Affiliation	Stephanie Kovalchik, RAND Corporation, USA
Date, Time	25 May 2016, 16:15-17:00
Location	HG G 26.5
Abstract	Sports forecasting models – beyond their interest to bettors – are important resources for sports analysts and coaches. Like the best athletes, the best forecasting models should be rigorously tested and judged by how well their performance holds up against top competitors. Although a number of models have been proposed for predicting match outcomes in professional tennis, their comparative performance is largely unknown. In this talk I will present results comparing the performance of 11 published forecasting models for predicting the outcomes of 2395 singles matches during the 2014 season of the Association of Tennis Professionals Tour. I’ll discuss the implications of these findings for current application of forecasting in tennis and how the advent of computer vision data will shape how prediction is used in future applications.

The Past, Present, and Future of Prediction in Professional Tennisread_more

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