{"id":640,"date":"2017-09-28T06:57:07","date_gmt":"2017-09-28T06:57:07","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&#038;p=640"},"modified":"2017-09-28T06:57:36","modified_gmt":"2017-09-28T06:57:36","slug":"short-review-multivariate-t-distribution","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/short-review-multivariate-t-distribution\/","title":{"rendered":"A short review of multivariate t-distribution"},"content":{"rendered":"<p>This paper reviews most important properties of a location-scale multivariate<br \/>\n<img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-6b97bb0f65c75b6cc0fba1868749478d_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#116;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"6\" style=\"vertical-align: 0px;\"\/>-distribution. A conditional representation of the distribution is exploited to<br \/>\noutline moments, characteristic function, marginal and conditional distributions,<br \/>\ndistribution of linear combinations and quadratic forms. Stochastic representation<br \/>\nis also used to determine the covariance matrix of the distribution. It also<br \/>\nmakes an attempt to justify an uncorrelated <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-6b97bb0f65c75b6cc0fba1868749478d_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#116;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"6\" style=\"vertical-align: 0px;\"\/>&#8211; model and overviews distribution<br \/>\nof the sum of products matrix and correlation matrix. Estimation strategies for<br \/>\nparameters of the model is briefly discussed. Finally the recent trend of linear<br \/>\nregression with the uncorrelated <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-6b97bb0f65c75b6cc0fba1868749478d_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#116;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"6\" style=\"vertical-align: 0px;\"\/>&#8211; model is discussed.<\/p>\n<p><a href=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/uploads\/40n1_5.pdf\">Fulltext<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper reviews most important properties of a location-scale multivariate -distribution. A conditional representation of the distribution is exploited to outline moments, characteristic function, marginal and conditional distributions, distribution of linear combinations and quadratic forms. Stochastic representation is also used to determine the covariance matrix of the distribution. It also makes an attempt to justify [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"issuem_issue":[20],"issuem_issue_categories":[],"issuem_issue_tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A short review of multivariate t-distribution - JSR<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jsr.isrt.ac.bd\/article\/short-review-multivariate-t-distribution\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A short review of multivariate t-distribution - JSR\" \/>\n<meta property=\"og:description\" content=\"This paper reviews most important properties of a location-scale multivariate -distribution. A conditional representation of the distribution is exploited to outline moments, characteristic function, marginal and conditional distributions, distribution of linear combinations and quadratic forms. Stochastic representation is also used to determine the covariance matrix of the distribution. 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