{"id":380,"date":"2017-05-12T11:10:44","date_gmt":"2017-05-12T11:10:44","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=380"},"modified":"2017-05-12T11:10:51","modified_gmt":"2017-05-12T11:10:51","slug":"variance-function-semi-parametric-analysis-count-data","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/","title":{"rendered":"Variance function in semi-parametric analysis of count data"},"content":{"rendered":"

The purpose of this paper is to determine an appropriate variance function
\n(mean-variance relationship) which can be used in the semi-parametric analy-
\nsis of over-dispersed count data (for example, for analysis of count data by ex-
\ntended quasi-likelihood and double extended quasi-likelihood). We use hypothesis
\ntesting approach through a broader class of models and data analytic approach.
\nThe models considered are the three parameter negative binomial distribution
\nand the extended quasi-likelihood. Wide analysis involving tests, data analysis
\nand simulations indicate that the three parameter generalized negative binomial
\ndistribution does not improve in \ft to count data over the simpler negative bi-
\nnomial distribution. Further data analysis and simulations using the extended
\nquasi-likelihood indicate that the negative binomial variance function \"\mu+c\mu^2\" is
\npreferable over a simpler variance function \"c_3\mu^2\" for data with small mean and
\nsmall over-dispersion. Otherwise \"c_3\mu^2\" is a preferable variance function over the
\nnegative binomial variance function.<\/p>\n

 <\/p>\n

44n1_9<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The purpose of this paper is to determine an appropriate variance function (mean-variance relationship) which can be used in the semi-parametric analy- sis of over-dispersed count data (for example, for analysis of count data by ex- tended quasi-likelihood and double extended quasi-likelihood). We use hypothesis testing approach through a broader class of models and data […]<\/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":[11],"issuem_issue_categories":[],"issuem_issue_tags":[],"class_list":["post-380","article","type-article","status-publish","format-standard","hentry","issuem_issue-v44n1"],"yoast_head":"\nVariance function in semi-parametric analysis of count data - 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\/variance-function-semi-parametric-analysis-count-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Variance function in semi-parametric analysis of count data - JSR\" \/>\n<meta property=\"og:description\" content=\"The purpose of this paper is to determine an appropriate variance function (mean-variance relationship) which can be used in the semi-parametric analy- sis of over-dispersed count data (for example, for analysis of count data by ex- tended quasi-likelihood and double extended quasi-likelihood). We use hypothesis testing approach through a broader class of models and data […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/\" \/>\n<meta property=\"og:site_name\" content=\"JSR\" \/>\n<meta property=\"article:modified_time\" content=\"2017-05-12T11:10:51+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/\",\"url\":\"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/\",\"name\":\"Variance function in semi-parametric analysis of count data - JSR\",\"isPartOf\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/#website\"},\"datePublished\":\"2017-05-12T11:10:44+00:00\",\"dateModified\":\"2017-05-12T11:10:51+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/#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\":\"Variance function in semi-parametric analysis of count data\"}]},{\"@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":"Variance function in semi-parametric analysis of count data - 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\/variance-function-semi-parametric-analysis-count-data\/","og_locale":"en_US","og_type":"article","og_title":"Variance function in semi-parametric analysis of count data - JSR","og_description":"The purpose of this paper is to determine an appropriate variance function (mean-variance relationship) which can be used in the semi-parametric analy- sis of over-dispersed count data (for example, for analysis of count data by ex- tended quasi-likelihood and double extended quasi-likelihood). We use hypothesis testing approach through a broader class of models and data […]","og_url":"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/","og_site_name":"JSR","article_modified_time":"2017-05-12T11:10:51+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/","url":"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/","name":"Variance function in semi-parametric analysis of count data - JSR","isPartOf":{"@id":"https:\/\/jsr.isrt.ac.bd\/#website"},"datePublished":"2017-05-12T11:10:44+00:00","dateModified":"2017-05-12T11:10:51+00:00","breadcrumb":{"@id":"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/jsr.isrt.ac.bd\/article\/variance-function-semi-parametric-analysis-count-data\/#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":"Variance function in semi-parametric analysis of count data"}]},{"@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"}]}},"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/article\/380","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/article"}],"about":[{"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/comments?post=380"}],"version-history":[{"count":0,"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/article\/380\/revisions"}],"wp:attachment":[{"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/media?parent=380"}],"wp:term":[{"taxonomy":"issuem_issue","embeddable":true,"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/issuem_issue?post=380"},{"taxonomy":"issuem_issue_categories","embeddable":true,"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/issuem_issue_categories?post=380"},{"taxonomy":"issuem_issue_tags","embeddable":true,"href":"http:\/\/jsr.isrt.ac.bd\/wp-json\/wp\/v2\/issuem_issue_tags?post=380"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}