TY - JOUR
ID - 5946
TI - Estimate the location matrix of a multivariate semiparametric regression model when the random error follows a matrix--variate generalized hyperbolic distribution
JO - International Journal of Nonlinear Analysis and Applications
JA - IJNAA
LA - en
SN - 2008-6822
AU - Salih, Sarmad Abdulkhaleq
AU - Aboudi, Emad Hazim
AD - Statistician at the Nineveh Agriculture Directorate, Mosul, Iraq
AD - Nineveh Agriculture Directorate, Mosul, Iraq.
Y1 - 2022
PY - 2022
VL - 13
IS - 1
SP - 2467
EP - 2482
KW - Multivariate Partial Linear Regression Model
KW - matrix-variate generalized hyperbolic distribution
KW - Kernel functions
KW - Smoothing Parameter
KW - Bayesian technique
DO - 10.22075/ijnaa.2022.5946
N2 - The matrix-variate generalized hyperbolic distribution is heavy-tailed mixed continuous skewed probability distribution. This distribution has multi applications in the field of economics, risk management, especially in stock modeling.This paper includes the estimate of the location matrix $\theta$ for the multivariate partial linear regression model, which is one of the multivariate semiparametric regression models when the random error follows a matrix-variate generalized hyperbolic distribution in the Bayesian technique depending on non-informative and informative prior information, estimating the location matrix under balanced and unbalanced loss function and the shape parameters ($\lambda ,\psi ,\nu $), skewness matrix ($\delta $), the scale matrix $(\Sigma)$ are known. In addition, estimation the smoothing parameter by a proposed method depending on the rule of thumb, the proposed kernel function depending on the mixed Gaussian kernel. the researchers concluded when non-informative and informative prior information is available that the posterior probability distribution for the location matrix $\theta$ is a matrix-variate generalized hyperbolic distribution, through the experimental side, it was found that the proposed kernel function is overriding than the Gaussian kernel function in estimate the location matrix and under informative prior information.
UR - https://ijnaa.semnan.ac.ir/article_5946.html
L1 - https://ijnaa.semnan.ac.ir/article_5946_a85b69d76fba0d8969f41c82f11c77c1.pdf
ER -