Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model
This research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution and found the statistical test of the model represented by finding the Bayes factor criterion...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
College of Computer Science and Mathematics, University of Mosul
2021-12-01
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| Series: | المجلة العراقية للعلوم الاحصائية |
| Online Access: | https://stats.mosuljournals.com/article_169967_395ff5244c598de93104afc1735d05a6.pdf |
| _version_ | 1828804084477460480 |
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| author | Sarmad Abdulkhaleq Salih Emad Aboudi |
| author_facet | Sarmad Abdulkhaleq Salih Emad Aboudi |
| author_sort | Sarmad Abdulkhaleq Salih |
| collection | DOAJ |
| description | This research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution and found the statistical test of the model represented by finding the Bayes factor criterion, the predictive distribution under assumption that the shape parameters are known. The prior distribution about the model parameters is represented by non-informative information, as well as the simulate on the generated data from the model by a suggested way based on different values of the shape parameters, the kernel function used in the generation was a Gaussian kernel function, the bandwidth (Smoothing) parameter was according to the rule of thumb. It found that the posterior marginal probability distribution of the location matrix and the predictive probability distribution is a matrix-t distribution with different parameters, the posterior marginal probability distribution of the scale matrix is proper distribution but it does not belong to the conjugate family, Through the Bayes factor criterion, it was found that the sample that was used in the generation process was drawn from a population that does not belong to the generalized modified Bessel population. |
| first_indexed | 2024-12-12T07:34:54Z |
| format | Article |
| id | doaj.art-49829f8f1ef1438d8d78c4d894a6f428 |
| institution | Directory Open Access Journal |
| issn | 1680-855X 2664-2956 |
| language | Arabic |
| last_indexed | 2024-12-12T07:34:54Z |
| publishDate | 2021-12-01 |
| publisher | College of Computer Science and Mathematics, University of Mosul |
| record_format | Article |
| series | المجلة العراقية للعلوم الاحصائية |
| spelling | doaj.art-49829f8f1ef1438d8d78c4d894a6f4282022-12-22T00:32:56ZaraCollege of Computer Science and Mathematics, University of Mosulالمجلة العراقية للعلوم الاحصائية1680-855X2664-29562021-12-011829111510.33899/iqjoss.2021.169967169967Bayesian Inference of a Non normal Multivariate Partial Linear Regression ModelSarmad Abdulkhaleq Salih0Emad Aboudi1PhD student University of Baghdad, College of Administration and EconomicsAssistant Professor, University of Baghdad, College of Administration and EconomicsThis research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution and found the statistical test of the model represented by finding the Bayes factor criterion, the predictive distribution under assumption that the shape parameters are known. The prior distribution about the model parameters is represented by non-informative information, as well as the simulate on the generated data from the model by a suggested way based on different values of the shape parameters, the kernel function used in the generation was a Gaussian kernel function, the bandwidth (Smoothing) parameter was according to the rule of thumb. It found that the posterior marginal probability distribution of the location matrix and the predictive probability distribution is a matrix-t distribution with different parameters, the posterior marginal probability distribution of the scale matrix is proper distribution but it does not belong to the conjugate family, Through the Bayes factor criterion, it was found that the sample that was used in the generation process was drawn from a population that does not belong to the generalized modified Bessel population.https://stats.mosuljournals.com/article_169967_395ff5244c598de93104afc1735d05a6.pdf |
| spellingShingle | Sarmad Abdulkhaleq Salih Emad Aboudi Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model المجلة العراقية للعلوم الاحصائية |
| title | Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model |
| title_full | Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model |
| title_fullStr | Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model |
| title_full_unstemmed | Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model |
| title_short | Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model |
| title_sort | bayesian inference of a non normal multivariate partial linear regression model |
| url | https://stats.mosuljournals.com/article_169967_395ff5244c598de93104afc1735d05a6.pdf |
| work_keys_str_mv | AT sarmadabdulkhaleqsalih bayesianinferenceofanonnormalmultivariatepartiallinearregressionmodel AT emadaboudi bayesianinferenceofanonnormalmultivariatepartiallinearregressionmodel |