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B0886
Title: Variable selection in robust heteroscedastic models with autoregressive covariance structures using EM-type algorithm Authors:  Yesim Guney - Ankara University (Turkey)
Fulya Gokalp Yavuz - Middle East Technical University (Turkey) [presenting]
Olcay Arslan - Ankara University (Turkey)
Abstract: The joint modeling of location and scatter matrix with multivariate $t$-distribution provides a valuable extension to the classical approach with normal distribution when the data set under consideration involves outliers or heavy tail outcomes. Variable selection is essential in these models since the covariance model has three models built into it, and the number of unknown parameters grows quadratically with the size of the matrix. The first purpose is to obtain the maximum likelihood estimates of the parameters and provide an expectation-maximization-type algorithm as an alternative to the Fisher scoring algorithm widely used for these models in the literature. Parameter estimation and variable selection are achieved simultaneously in a multivariate $t$ joint location and scatter matrix model using shrinkage approaches. To assess the performance of the considered methods, we conducted a simulation study and real data analysis.