ZüKoSt: Seminar on Applied Statistics

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Autumn Semester 2016

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Date / Time Speaker Title Location
15 September 2016
Emmanuel Lessaffre

Modeling multivariate multilevel continuous responses with a hierarchical regression model for the mean and covariance matrix applied to a large nursing data set  HG  G 19.1 
Abstract: We propose a novel multivariate multilevel model that expresses both the mean and covariance structure as a multivariate mixed effects model. We called this the multilevel covariance regression (MCR) model. Two versions of this model are presented. In the first version the covariance matrix of the multivariate response is allowed to depend on covariates and random effects. In this model the random effects of the covariance part are assumed to be independent of random effects of the mean structure. In the second model this assumption is relaxed by allowing the two types of random effects to be dependent. The motivating data set is obtained from the RN4CAST (Sermeus et al. 2011) FP7 project which involves 33,731 registered nurses in 2,169 nursing units in 486 hospitals in 12 European countries. As response we have taken the three classical burnout dimensions (Maslach and Jackson, 1981) extracted from a 22-item questionnaire, i.e. emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). There are four levels in the total data set: nurses, nursing units, hospitals and (for the whole data set) countries. The first model is applied to the total data set, while the second model is applied to only the Belgian part of the data. The two models address the following nurse research questions simultaneously: 1) how much variation of burnout could be explained by the level-specific fixed and random effects? 2) do the variances and correlations among burnout stay constant across level-specific characteristics and units at each level? The two models are explored with respect to their statistical properties, but are also compared on the Belgian part of the study. We opted for the Bayesian approach to estimate the parameters of the model. To this end we made use of the JAGS Markov chain Monte Carlo program through the R package rjags.
13 October 2016
Torsten Hothorn

Understanding and Applying Transformation Models  HG G 19.1 
Abstract: Transformation models are a surprisingly large and useful class of models for conditional and also unconditional distributions. Many known transformation models, for example the Cox proportional hazards model or proportional odds logistic regression, have been known for decades in survival or categorical data analysis. The strong connections between these models and other commonly used procedures, for example normal or binary linear models, are not very well known. It is very stimulating, both from an intellectual and a practical point of view, to interpret such classical models as transformation models and therefore as models for describing distributions instead of means. We will look at a cascade ranging from very simple to rather complex unconditional and conditional transformation models theoretically and practically. The R add-on package "mlt" (Most Likely Transformations) allows fitting many of such transformation models in the maximum likelihood framework and will be used to illustrate how one can estimate and analyse interesting transformations models in R. References http://dx.doi.org/10.1111/rssb.12017 http://arxiv.org/abs/1508.06749 http://CRAN.R-project.org/package=mlt https://cran.r-project.org/web/packages/mlt.docreg/vignettes/mlt.pdf
20 October 2016
Thomas Hofmann

latent variable models for natural language understanding HG G 19.1 
8 December 2016
Nicolas Städler

Title T.B.A. HG G 19.1 

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