{"id":3002,"date":"2018-09-18T13:19:37","date_gmt":"2018-09-18T17:19:37","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2\/?page_id=3002"},"modified":"2020-10-13T19:21:43","modified_gmt":"2020-10-13T23:21:43","slug":"fresmem","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/","title":{"rendered":"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit"},"content":{"rendered":"<div id=\"content1\">\n<div id=\"main\">\n<div id=\"right\">\n<div id=\"right_text\">\n<div class=\"box\">\n<p>We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood<br \/>\n(MPL) estimation along with the smoothly clipped absolute deviation\u00a0(SCAD) and adaptive least absolute shrinkage and selection operator\u00a0(ALASSO) penalty functions. The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model selection criterion, called the ICQ statistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu, and Tang, 2008, Journal of the American Statistical Association 103, 1648-1658). The variable selection procedure based on ICQ is shown to consistently select important fixed and random effects. The methodology is very general and can be applied to numerous situations involving random effects, including generalized linear mixed models. Simulation studies and a real data set from a Yale infant growth study are used to illustrate the proposed methodology.<\/p>\n<p><a href=\"http:\/\/www.bios.unc.edu\/research\/bias\/documents\/RXdocuments.rar\"><strong><a href=\"https:\/\/www.med.unc.edu\/bigs2\/wp-content\/uploads\/sites\/822\/2020\/10\/RXdocuments.zip\">Software Download<\/a><\/strong><\/a><\/p>\n<p><strong>Citation<\/strong>: Joseph G. Ibrahim, Hongtu Zhu, Ramon I. Garcia, and Ruixin Guo. Fixed and Random Effects Selection in Mixed Effects Models&#8221;. <em>Biometrics<\/em>. 2011.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"footer\">\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<p><!-- footer ends--><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation\u00a0(SCAD) and adaptive least absolute shrinkage and selection operator\u00a0(ALASSO) penalty functions. The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model &hellip; <a href=\"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/\" aria-label=\"Read more about FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit\">Read more<\/a><\/p>\n","protected":false},"author":1503,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-3002","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation\u00a0(SCAD) and adaptive least absolute shrinkage and selection operator\u00a0(ALASSO) penalty functions. The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model &hellip; Read more\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/\" \/>\n<meta property=\"og:site_name\" content=\"BIG-S2\" \/>\n<meta property=\"article:modified_time\" content=\"2020-10-13T23:21:43+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:\/\/www.med.unc.edu\/bigs2\/fresmem\/\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/\",\"name\":\"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit - BIG-S2\",\"isPartOf\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\"},\"datePublished\":\"2018-09-18T17:19:37+00:00\",\"dateModified\":\"2020-10-13T23:21:43+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.med.unc.edu\/bigs2\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/\",\"name\":\"BIG-S2\",\"description\":\"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit - BIG-S2","robots":{"index":"noindex","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"og_locale":"en_US","og_type":"article","og_title":"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit - BIG-S2","og_description":"We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation\u00a0(SCAD) and adaptive least absolute shrinkage and selection operator\u00a0(ALASSO) penalty functions. The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model &hellip; Read more","og_url":"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/","og_site_name":"BIG-S2","article_modified_time":"2020-10-13T23:21:43+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/","url":"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/","name":"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit - BIG-S2","isPartOf":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/#website"},"datePublished":"2018-09-18T17:19:37+00:00","dateModified":"2020-10-13T23:21:43+00:00","breadcrumb":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.med.unc.edu\/bigs2\/fresmem\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.med.unc.edu\/bigs2\/fresmem\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.med.unc.edu\/bigs2\/"},{"@type":"ListItem","position":2,"name":"FRESMEM: Fixed and Random Effects Selection in Mixed Effects Toolkit"}]},{"@type":"WebSite","@id":"https:\/\/www.med.unc.edu\/bigs2\/#website","url":"https:\/\/www.med.unc.edu\/bigs2\/","name":"BIG-S2","description":"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links_to":[],"_links_to_target":[],"_links":{"self":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/3002","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/users\/1503"}],"replies":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/comments?post=3002"}],"version-history":[{"count":0,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/3002\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/media?parent=3002"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}