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In this paper we introduce robust techniques for inference and model selection in the analysis of longitudinal data. Robust versions of quasi-likelihood functions are obtained by building upon a set of robust estimating equations where robustness is achieved by weighting the classical estimating equations. The robust quasi-likelihood functions are then used(More)
Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy for(More)
By means of the indirect immunoperoxidase method glucocorticoid receptor (GR) immunoreactive nerve cells of the lower brain stem and the spinal cord have been mapped out in the rat, using a monoclonal antibody against rat liver GR. The GR immunoreactivity was predominantly located within the nuclei of these nerve cell bodies but also in glial cells of the(More)
In this paper robust statistical procedures are presented for the analysis of skewed and heavy-tailed outcomes as they typically occur in health care data. The new estimators and test statistics are extensions of classical maximum likelihood techniques for generalized linear models. In contrast to their classical counterparts, the new robust techniques show(More)
Litter characteristics at birth were recorded in 4 genetic types of sows with differing maternal abilities. Eighty-two litters from F(1) Duroc x Large White sows, 651 litters from Large White sows, 63 litters from Meishan sows, and 173 litters from Laconie sows were considered. Statistical models included random effects of sow, litter, or both; fixed(More)
Acknowledgments This present work, similar to a hike in the mountains of Switzerland, has taken time to achieve. This long-term project leaves some memories of people to whom I would like to express my gratitude. I would like to express my deepest gratitude to my supervisor, Professor Stephan Morgenthaler for his continuous encouragement, patience, guidance(More)
The Institute for Labour Market Policy Evaluation (IFAU) is a research institute under the Swedish Ministry of Industry, Employment and Communications , situated in Uppsala. IFAU's objective is to promote, support and carry out: evaluations of the effects of labour market policies, studies of the functioning of the labour market and evaluations of the(More)
We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density(More)