ZüKoSt: Seminar on Applied Statistics

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Spring Semester 2016

Note: The highlighted event marks the next occurring event.

Date / Time Speaker Title Location
27 January 2016
Simon Wood
University of Bath
Smoothing parameter and model selection for general smooth models  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.
7 April 2016
Christoph Buser

Title T.B.A. HG G 19.1 
27 April 2016
Martyn Plummer
International Agency for Research on Cancer, Lyon, France
Feedback and Modularization in Bayesian Models  HG G 19.1 
Abstract: tba
11 May 2016
Andrea Riebler
NTNU, Norway
Title T.B.A.  HG G 19.1 
Abstract: tba
25 May 2016
Stephanie Kovalchik
RAND Corporation, USA
Title T.B.A. HG ? ? 

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