Bayesian Statistical Inference For Laplacian Class of Matrix Variate Elliptically Contoured Models

M. Arashi, A. K. MD. Ehsanes Saleh, Daya K. Nagar and S. M. M. Tabatabaey

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 13, 2774-2787

Abstract: In the context of a subclass of matrix variate elliptically contoured (MEC) models, namely Laplacian MEC, with location vector μ$\boldsymbol{\mu }$ and dispersion matrix Σ$\boldsymbol{\Sigma }$, where both are unknown, Bayesian inference is considered through vague prior knowledge firstly. At the second step, an informative prior is incorporated to derive posterior distributions of μ$\boldsymbol{\mu }$ and Σ$\boldsymbol{\Sigma }$. Afterward, the main result is thoroughly considered for matrix variate Student’s t-model and thus generalizing the result of Arnold Zellner (Zellner, 1976).

Date: 2015
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DOI: 10.1080/03610926.2013.799694

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