Bayes estimators of a multivariate generalized hyperbolic partial regression model

Saliha, Sarmad Abdulkhaleq; Aboudib, Emad Hazim

doi:10.22075/ijnaa.2021.5166

Bayes estimators of a multivariate generalized hyperbolic partial regression model

Document Type : Research Paper

Authors

Sarmad Abdulkhaleq Saliha ¹

Emad Hazim Aboudib ²

¹ Statistician at the Nineveh Agriculture Directorate, Mosul, Iraq

² College of Administration and Economics, University of Baghdad, Baghdad, Iraq

10.22075/ijnaa.2021.5166

Abstract

The matrix-variate generalized hyperbolic distribution belongs to the family of heavy-tailed mixed probability distributions and is considered to be one of the continuous skewed probability distributions. This distribution has wide applications in the field of economics, especially in stock modeling. This paper includes estimation the parameters of the multivariate semi-parametric regression model represented by the multivariate partial linear regression model when the random error follows the matrix-variate generalized hyperbolic distribution, using the Bayesian method when non-informative prior information is available and under the assumption that the shape parameters and the skewness matrix are known. In addition, the bandwidth parameter is estimated by a suggested way based on the normal distribution rule and the proposed kernel function based on the mixed Gaussian kernel function and studying the findings on the generated data in a way suggested for the model, comparing the estimators depending on the criterion of the mean sum of squares error. The two researchers concluded that the proposed kernel function is better than the Gaussian kernel
function in estimate the parameters.

Keywords

matrix-variate generalized hyperbolic distribution

multivariate partial regression model

kernel functions, bandwidth parameter

Bayes method

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