{"id":368,"date":"2017-05-12T10:57:01","date_gmt":"2017-05-12T10:57:01","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&#038;p=368"},"modified":"2017-05-12T10:57:38","modified_gmt":"2017-05-12T10:57:38","slug":"controlling-average-false-discovery-large-scale-multiple-testing","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/controlling-average-false-discovery-large-scale-multiple-testing\/","title":{"rendered":"Controlling the average false discovery in large-scale multiple testing"},"content":{"rendered":"<p>In this paper, we consider multiple testing procedures in which we simultaneously test a large number m of null hypotheses <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-29b4c32dff560aacf0bbab0968004998_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#72;&#95;&#49;&#44;&#92;&#108;&#100;&#111;&#116;&#115;&#44;&#32;&#72;&#95;&#109;\" title=\"Rendered by QuickLaTeX.com\" height=\"16\" width=\"89\" style=\"vertical-align: -4px;\"\/> using the test statistics <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-27f727f447c2421576dd34d9a04e6638_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#84;&#95;&#49;&#44;&#32;&#92;&#108;&#100;&#111;&#116;&#115;&#44;&#32;&#84;&#95;&#109;\" title=\"Rendered by QuickLaTeX.com\" height=\"16\" width=\"79\" style=\"vertical-align: -4px;\"\/>. The currently used procedure of controlling the false discovery rate (FDR) at a specifi\fed level requires that the statistics <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-27f727f447c2421576dd34d9a04e6638_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#84;&#95;&#49;&#44;&#32;&#92;&#108;&#100;&#111;&#116;&#115;&#44;&#32;&#84;&#95;&#109;\" title=\"Rendered by QuickLaTeX.com\" height=\"16\" width=\"79\" style=\"vertical-align: -4px;\"\/> be either independently distributed or positively related. In practice Ti&#8217;s are rarely independent and it is not known how to ascertain the positive relationship between<br \/>\n<img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-3f52a689041f1c1ef346211fd6cb4208_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#84;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"15\" width=\"15\" style=\"vertical-align: -3px;\"\/>&#8216;s. In this paper, we propose to control the expected value of the Average False<br \/>\nDiscovery (AFD) at some speci\fed level. This AFD procedure controls its level<br \/>\nat the specifi\fed value independent of how <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-3f52a689041f1c1ef346211fd6cb4208_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#84;&#95;&#105;\" title=\"Rendered by QuickLaTeX.com\" height=\"15\" width=\"15\" style=\"vertical-align: -3px;\"\/>&#8216;s are related. This specifi\fed value<br \/>\ncan be chosen to control <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-9dc53f8ecc1bcf15020c6df4c12f1c27_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#107;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"9\" style=\"vertical-align: 0px;\"\/>-FWER or <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-2afeb87159a4918337409f4bf56d689a_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#103;&#97;&#109;&#109;&#97;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"10\" style=\"vertical-align: -4px;\"\/>FWER and even FDR at their respective<br \/>\nspecifi\fed levels. Using simulation, we compare our proposed AFD procedure with<br \/>\nthe FDR procedure. In terms of power and stability, the proposed AFD procedure<br \/>\nhas an edge over the FDR procedure, as well as over <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-9dc53f8ecc1bcf15020c6df4c12f1c27_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#107;\" title=\"Rendered by QuickLaTeX.com\" height=\"12\" width=\"9\" style=\"vertical-align: 0px;\"\/>-FWER procedure. Two<br \/>\nillustrative examples are given to compare the number of di\u000berentially expressed<br \/>\ngenes obtained by the two methods.<\/p>\n<p><a href=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/uploads\/44n1_5.pdf\">44n1_5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we consider multiple testing procedures in which we simultaneously test a large number m of null hypotheses using the test statistics . The currently used procedure of controlling the false discovery rate (FDR) at a specifi\fed level requires that the statistics be either independently distributed or positively related. In practice Ti&#8217;s are [&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":[11],"issuem_issue_categories":[],"issuem_issue_tags":[],"class_list":["post-368","article","type-article","status-publish","format-standard","hentry","issuem_issue-v44n1"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Controlling the average false discovery in large-scale multiple testing - 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\/controlling-average-false-discovery-large-scale-multiple-testing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Controlling the average false discovery in large-scale multiple testing - JSR\" \/>\n<meta property=\"og:description\" content=\"In this paper, we consider multiple testing procedures in which we simultaneously test a large number m of null hypotheses using the test statistics . The currently used procedure of controlling the false discovery rate (FDR) at a specifi ed level requires that the statistics be either independently distributed or positively related. In practice Ti&#8217;s are [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jsr.isrt.ac.bd\/article\/controlling-average-false-discovery-large-scale-multiple-testing\/\" \/>\n<meta property=\"og:site_name\" content=\"JSR\" \/>\n<meta property=\"article:modified_time\" content=\"2017-05-12T10:57:38+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\/controlling-average-false-discovery-large-scale-multiple-testing\/\",\"url\":\"https:\/\/jsr.isrt.ac.bd\/article\/controlling-average-false-discovery-large-scale-multiple-testing\/\",\"name\":\"Controlling the average false discovery in large-scale multiple testing - JSR\",\"isPartOf\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/#website\"},\"datePublished\":\"2017-05-12T10:57:01+00:00\",\"dateModified\":\"2017-05-12T10:57:38+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/controlling-average-false-discovery-large-scale-multiple-testing\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/jsr.isrt.ac.bd\/article\/controlling-average-false-discovery-large-scale-multiple-testing\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/controlling-average-false-discovery-large-scale-multiple-testing\/#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\":\"Controlling the average false discovery in large-scale multiple testing\"}]},{\"@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":"Controlling the average false discovery in large-scale multiple testing - 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\/controlling-average-false-discovery-large-scale-multiple-testing\/","og_locale":"en_US","og_type":"article","og_title":"Controlling the average false discovery in large-scale multiple testing - JSR","og_description":"In this paper, we consider multiple testing procedures in which we simultaneously test a large number m of null hypotheses using the test statistics . 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