Bayes estimators of a multivariate generalized hyperbolic partial regression model

سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 84

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شناسه ملی سند علمی:

JR_IJNAA-12-2_076

تاریخ نمایه سازی: 11 آذر 1401

چکیده مقاله:

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.  

کلیدواژه ها:

matrix-variate generalized hyperbolic distribution ، multivariate partial regression model ، kernel functions ، bandwidth parameter ، Bayes method

نویسندگان

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Statistician at the Nineveh Agriculture Directorate, Mosul, Iraq

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