Some simulation/computation in multivariate linear models of scale mixtures of skew-normal-Cauchy distributions

Abstract

In this work, we extend standard likelihood-based procedures to the multivariate linear model using the scale mixtures of multivariate skew-normal-Cauchy distributions. A simple EM algorithm for iteratively computing maximum likelihood estimates is derived. The observed information matrix is computed analytically to account for standard errors. Some results are obtained from real and simulated datasets to illustrate the usefulness of the proposed model.

Keywords:

• EM-algorithm
• Multivariate linear regression models
• Scale mixtures skew-normal-Cauchy distributions
• Shape mixture

MATHEMATICAL SUBJECT CLASSIFICATION:

• 62E99
• 62H10
• 62H12
• 62J05

Additional information

Funding

Fereshte Kahrari was supported partially by “Beca del programa postdoctoral 2018” from University of Atacama. The work of Clecio Ferreira was supported by the National Council for Scientific and Technological Development (CNPq), Brazil.