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Robust linear mixed effects models with scale mixtures of skew-normal distributions

Grant number: 12/03590-0
Support type:Research Grants - Visiting Researcher Grant - International
Duration: July 29, 2012 - August 17, 2012
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Principal Investigator:Víctor Hugo Lachos Dávila
Grantee:Víctor Hugo Lachos Dávila
Visiting researcher: Dipak Kumar Dey
Visiting researcher institution: University of Connecticut (UCONN), United States
Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

The objective of this project is to present an inferential Bayesian study in linear and nonlinear mixed models using more robust distributions than the skew-normal distribution, that is, using the class of scale of mixture of skew-normal distributions. The new model will be referred as SMSN-LMM. Moreover, diagnostics studies will be presented based on the divergence measure of Kullback--Leibler, like discussed in Lachos, Bandyopadhyay and Dey (2011). In the estimation process, a Gibbs sampler will be used with implementation in R, C++ and WinBUGS. The purpose of this project is to contribute positively to the development in the statistical research field, creating new results in models with practical interest, extending and complementing some of the skew-normal results found, for example, Lachos, Bandyopadhyay and Dey (2011) and others. (AU)