Akademik Veri Yönetim Sistemi
15th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2022), London, İngiltere, 17 - 19 Aralık 2022, ss.100
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.