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Bayes estimators of a multivariate generalized hyperbolic partial regression model | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 961-975 اصل مقاله (516.93 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5166 | ||
| نویسندگان | ||
| Sarmad Abdulkhaleq Saliha* 1؛ Emad Hazim Aboudib2 | ||
| 1Statistician at the Nineveh Agriculture Directorate, Mosul, Iraq | ||
| 2College of Administration and Economics, University of Baghdad, Baghdad, Iraq | ||
| تاریخ دریافت: 26 بهمن 1399، تاریخ بازنگری: 01 فروردین 1400، تاریخ پذیرش: 04 خرداد 1400 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| matrix-variate generalized hyperbolic distribution؛ multivariate partial regression model؛ kernel functions, bandwidth parameter؛ Bayes method | ||
| مراجع | ||
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