{"id":5019,"date":"2025-06-24T12:51:14","date_gmt":"2025-06-24T12:51:14","guid":{"rendered":"https:\/\/fs.kibu.ac.ke\/?p=5019"},"modified":"2025-12-26T23:29:39","modified_gmt":"2025-12-26T23:29:39","slug":"an-m-stage-hierachical-group-testing-model-for-estimating-multiple-traits-in-a-population","status":"publish","type":"post","link":"https:\/\/kibu.ac.ke\/fs\/an-m-stage-hierachical-group-testing-model-for-estimating-multiple-traits-in-a-population\/","title":{"rendered":"An M \u2013 Stage Hierachical Group Testing Model for Estimating Multiple Traits in a Population"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"5019\" class=\"elementor elementor-5019\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6d5388e1 elementor-section-full_width elementor-section-height-min-height elementor-section-items-stretch elementor-section-height-default\" data-id=\"6d5388e1\" data-element_type=\"section\" id=\"ourgoal\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5fbb1e76\" data-id=\"5fbb1e76\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-64c01ebf elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"64c01ebf\" data-element_type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">An M \u2013 Stage Hierachical Group Testing Model for Estimating Multiple Traits in a Population<\/h1>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f328d7e elementor-section-height-min-height elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"f328d7e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4c373519\" data-id=\"4c373519\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-18eee744 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"18eee744\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-3fd89977 elementor-invisible\" data-id=\"3fd89977\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4d773c3c elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"4d773c3c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2020<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-49fde886 elementor-invisible\" data-id=\"49fde886\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4452769c elementor-widget elementor-widget-heading\" data-id=\"4452769c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">KIBU Authors<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-770ef3f6 elementor-invisible elementor-widget elementor-widget-text-editor\" data-id=\"770ef3f6\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Sirengo L. John<\/p><p>Away Shem<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-127e2dd8 elementor-invisible\" data-id=\"127e2dd8\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2774751f elementor-tablet-align-left elementor-invisible elementor-widget elementor-widget-button\" data-id=\"2774751f\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInLeft&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-shrink\" href=\"https:\/\/www.iosrjournals.org\/iosr-jm\/papers\/Vol16-issue3\/Series-2\/F1603022934.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">VIEW ON PUBLISHER SITE<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-1dfbd9ca elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1dfbd9ca\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-1a016fc9 elementor-invisible\" data-id=\"1a016fc9\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4a7a6b98 elementor-widget elementor-widget-heading\" data-id=\"4a7a6b98\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-55ca7b97 elementor-invisible elementor-widget elementor-widget-text-editor\" data-id=\"55ca7b97\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Monitoring the presence or absence of a trait in a large population one \u2013 at \u2013 a \u2013 time is tedious, uneconomical and bound to errors. The remedy is to group the population into homogeneous groups and test each group for the presence of a trait. Multi \u2013 stage group testing procedure involves testing groups for the presence or absence of trait in a population and sequentially subdividing the positive groups into sub \u2013 groups. The sub \u2013 groups to be tested at a particular stage are based on the information obtained from the previous stage. This paper proposed an M \u2013 stage hierarchical design for testing the presence of multiple traits in a finite population. The design improves the efficiency of the estimators as evident via the computation of asymptotic variance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>2020 KIBU Authors Sirengo L. John Away Shem VIEW ON PUBLISHER SITE Abstract Monitoring the presence or absence of a trait in a large population one \u2013 at \u2013 a \u2013 time is tedious, uneconomical and bound to errors. The remedy is to group the population into homogeneous groups and test each group for the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-5019","post","type-post","status-publish","format-standard","hentry","category-research-list"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>An M \u2013 Stage Hierachical Group Testing Model for Estimating Multiple Traits in a Population - Faculty of Science<\/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:\/\/kibu.ac.ke\/fs\/an-m-stage-hierachical-group-testing-model-for-estimating-multiple-traits-in-a-population\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An M \u2013 Stage Hierachical Group Testing Model for Estimating Multiple Traits in a Population - Faculty of Science\" \/>\n<meta property=\"og:description\" content=\"2020 KIBU Authors Sirengo L. John Away Shem VIEW ON PUBLISHER SITE Abstract Monitoring the presence or absence of a trait in a large population one \u2013 at \u2013 a \u2013 time is tedious, uneconomical and bound to errors. 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John Away Shem VIEW ON PUBLISHER SITE Abstract Monitoring the presence or absence of a trait in a large population one \u2013 at \u2013 a \u2013 time is tedious, uneconomical and bound to errors. 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