{"id":179,"date":"2016-10-26T23:07:58","date_gmt":"2016-10-26T23:07:58","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&#038;p=179"},"modified":"2016-10-26T23:41:20","modified_gmt":"2016-10-26T23:41:20","slug":"bivariate-version-hyper-poisson-distribution-properties","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/","title":{"rendered":"A bivariate version of the hyper-Poisson distribution and some of its properties"},"content":{"rendered":"<p>A bivariate version of the hyper-Poisson distribution is introduced here through its probability generating function (pgf). we study some of its important aspects by deriving its probability mass function, factorial moments, marginal and conditional distributions and obtain certain recurrence relations for its probabilities, raw moments and factorial moments. Further, the method of maximum likelihood is discussed and the procedures are illustrated using a real life data set.<\/p>\n<p><a href=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/uploads\/48_50n1_5.pdf\">Fulltext<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A bivariate version of the hyper-Poisson distribution is introduced here through its probability generating function (pgf). we study some of its important aspects by deriving its probability mass function, factorial moments, marginal and conditional distributions and obtain certain recurrence relations for its probabilities, raw moments and factorial moments. Further, the method of maximum likelihood is [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"comment_status":"open","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":[5],"issuem_issue_categories":[],"issuem_issue_tags":[],"class_list":["post-179","article","type-article","status-publish","format-standard","hentry","issuem_issue-v48_50n1"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A bivariate version of the hyper-Poisson distribution and some of its properties - 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\/bivariate-version-hyper-poisson-distribution-properties\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A bivariate version of the hyper-Poisson distribution and some of its properties - JSR\" \/>\n<meta property=\"og:description\" content=\"A bivariate version of the hyper-Poisson distribution is introduced here through its probability generating function (pgf). we study some of its important aspects by deriving its probability mass function, factorial moments, marginal and conditional distributions and obtain certain recurrence relations for its probabilities, raw moments and factorial moments. Further, the method of maximum likelihood is [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/\" \/>\n<meta property=\"og:site_name\" content=\"JSR\" \/>\n<meta property=\"article:modified_time\" content=\"2016-10-26T23:41:20+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/\",\"url\":\"https:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/\",\"name\":\"A bivariate version of the hyper-Poisson distribution and some of its properties - JSR\",\"isPartOf\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/#website\"},\"datePublished\":\"2016-10-26T23:07:58+00:00\",\"dateModified\":\"2016-10-26T23:41:20+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/jsr.isrt.ac.bd\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Articles\",\"item\":\"https:\/\/jsr.isrt.ac.bd\/article\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"A bivariate version of the hyper-Poisson distribution and some of its properties\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/#website\",\"url\":\"https:\/\/jsr.isrt.ac.bd\/\",\"name\":\"JSR\",\"description\":\"Journal of Statistical Research\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/jsr.isrt.ac.bd\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"A bivariate version of the hyper-Poisson distribution and some of its properties - JSR","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/jsr.isrt.ac.bd\/article\/bivariate-version-hyper-poisson-distribution-properties\/","og_locale":"en_US","og_type":"article","og_title":"A bivariate version of the hyper-Poisson distribution and some of its properties - JSR","og_description":"A bivariate version of the hyper-Poisson distribution is introduced here through its probability generating function (pgf). we study some of its important aspects by deriving its probability mass function, factorial moments, marginal and conditional distributions and obtain certain recurrence relations for its probabilities, raw moments and factorial moments. 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