{"id":654,"date":"2017-09-28T08:23:21","date_gmt":"2017-09-28T08:23:21","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&#038;p=654"},"modified":"2017-09-28T08:24:18","modified_gmt":"2017-09-28T08:24:18","slug":"improved-estimators-logistic-regression-model","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/improved-estimators-logistic-regression-model\/","title":{"rendered":"Some improved estimators in logistic regression model"},"content":{"rendered":"<p>The problem of estimating the parameters of logistic regression model is considered<br \/>\nwhen it is known from extraneous sources that the <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-42b1c1e35819acf41ad297158595bc66_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#116;&#101;&#120;&#116;&#105;&#116;&#32;&#123;&#117;&#110;&#99;&#101;&#114;&#116;&#97;&#105;&#110;&#32;&#112;&#114;&#105;&#111;&#114;&#32;&#105;&#110;&#102;&#111;&#114;&#109;&#97;&#116;&#105;&#111;&#110;&#125; &#105;&#110;&#32;&#116;&#104;&#101;&#32;&#102;&#111;&#114;&#109;&#32;&#111;&#102;&#32;&#116;&#104;&#101;&#32;&#104;&#121;&#112;&#111;&#116;&#104;&#101;&#115;&#105;&#115;\" title=\"Rendered by QuickLaTeX.com\" height=\"16\" width=\"427\" style=\"vertical-align: -4px;\"\/>H_0:\\beta_0=\\ldots, =\\beta_{k-1}=\\beta^0<img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-f8edce40304cb8fa3973cbe075d87474_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#40;&#112;&#105;&#118;&#111;&#116;&#41;&#32;&#109;&#97;&#121; &#104;&#111;&#108;&#100;&#46;&#32;&#70;&#105;&#118;&#101;&#32;&#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114;&#115;&#44;&#32;&#110;&#97;&#109;&#101;&#108;&#121;&#44;&#32;&#116;&#104;&#101;&#32;&#117;&#110;&#114;&#101;&#115;&#116;&#114;&#105;&#99;&#116;&#101;&#100;&#32;&#109;&#97;&#120;&#105;&#109;&#117;&#109;&#32;&#108;&#105;&#107;&#101;&#108;&#105;&#104;&#111;&#111;&#100;&#32;&#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114; &#40;&#85;&#77;&#76;&#69;&#41;&#44;&#32;&#116;&#104;&#101;&#32;&#115;&#104;&#114;&#105;&#110;&#107;&#97;&#103;&#101;&#32;&#114;&#101;&#115;&#116;&#114;&#105;&#99;&#116;&#101;&#100;&#32;&#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114;&#32;&#40;&#83;&#82;&#69;&#41;&#44;&#32;&#116;&#104;&#101;&#32;&#115;&#104;&#114;&#105;&#110;&#107;&#97;&#103;&#101;&#32;&#112;&#114;&#101;&#108;&#105;&#109;&#105;&#110;&#97;&#114;&#121;&#32;&#116;&#101;&#115;&#116; &#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114;&#32;&#40;&#83;&#80;&#84;&#69;&#41;&#44;&#32;&#116;&#104;&#101;&#32;&#115;&#104;&#114;&#105;&#110;&#107;&#97;&#103;&#101;&#32;&#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114;&#32;&#40;&#83;&#69;&#41;&#32;&#97;&#110;&#100;&#32;&#116;&#104;&#101;&#32;&#112;&#111;&#115;&#105;&#116;&#105;&#118;&#101;&#45;&#114;&#117;&#108;&#101;&#32;&#115;&#104;&#114;&#105;&#110;&#107;&#97;&#103;&#101; &#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114;&#32;&#40;&#83;&#69;&#43;&#41;&#32;&#97;&#114;&#101;&#32;&#99;&#111;&#110;&#115;&#105;&#100;&#101;&#114;&#101;&#100;&#46;&#32;&#84;&#104;&#101;&#32;&#83;&#69;&#32;&#97;&#110;&#100;&#32;&#83;&#69;&#43;&#32;&#97;&#114;&#101;&#32;&#116;&#104;&#101;&#32;&#83;&#116;&#101;&#105;&#110;&#45;&#116;&#121;&#112;&#101;&#32;&#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114;&#115; &#98;&#97;&#115;&#101;&#100;&#32;&#111;&#110;&#32;&#116;&#104;&#101;&#32;&#112;&#114;&#101;&#108;&#105;&#109;&#105;&#110;&#97;&#114;&#121;&#32;&#116;&#101;&#115;&#116;&#32;&#97;&#112;&#112;&#114;&#111;&#97;&#99;&#104;&#32;&#111;&#102;&#32;&#83;&#97;&#108;&#101;&#104;&#32;&#97;&#110;&#100;&#32;&#83;&#101;&#110;&#46;&#32;&#73;&#110;&#32;&#116;&#104;&#101;&#32;&#108;&#105;&#103;&#104;&#116;&#32;&#111;&#102;&#32;&#100;&#101;&#114;&#105;&#118;&#101;&#100; &#77;&#83;&#69;&#32;&#109;&#97;&#116;&#114;&#105;&#99;&#101;&#115;&#32;&#97;&#110;&#100;&#32;&#100;&#105;&#115;&#116;&#114;&#105;&#98;&#117;&#116;&#105;&#111;&#110;&#97;&#108;&#32;&#114;&#105;&#115;&#107;&#115;&#44;&#32;&#116;&#104;&#101;&#32;&#114;&#101;&#108;&#97;&#116;&#105;&#118;&#101;&#32;&#112;&#101;&#114;&#102;&#111;&#114;&#109;&#97;&#110;&#99;&#101;&#32;&#111;&#102;&#32;&#116;&#104;&#101;&#32;&#102;&#105;&#118;&#101;&#32;&#101;&#115;&#116;&#105;&#109;&#97;&#116;&#111;&#114;&#115; &#117;&#110;&#100;&#101;&#114;&#32;&#108;&#111;&#99;&#97;&#108;&#32;&#97;&#108;&#116;&#101;&#114;&#110;&#97;&#116;&#105;&#118;&#101;&#115;&#32;&#97;&#114;&#101;&#32;&#115;&#116;&#117;&#100;&#105;&#101;&#100;&#32;&#105;&#110;&#32;&#100;&#101;&#116;&#97;&#105;&#108;&#46;&#32;&#84;&#104;&#101;&#115;&#101;&#32;&#97;&#110;&#97;&#108;&#121;&#115;&#101;&#115;&#32;&#114;&#101;&#118;&#101;&#97;&#108;&#32;&#116;&#104;&#97;&#116; &#119;&#104;&#101;&#110;\" title=\"Rendered by QuickLaTeX.com\" height=\"62\" width=\"2039\" style=\"vertical-align: -4px;\"\/>k\u00a0 \\geq 3<img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-9ca1ac84f1708849911f553065e0492f_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#44;&#32;&#119;&#101;&#32;&#115;&#104;&#111;&#117;&#108;&#100;&#32;&#117;&#115;&#101;&#32;&#116;&#104;&#101;&#32;&#83;&#69;&#32;&#111;&#114;&#32;&#83;&#69;&#43;&#32;&#97;&#110;&#100;&#32;&#102;&#111;&#114;\" title=\"Rendered by QuickLaTeX.com\" height=\"16\" width=\"277\" style=\"vertical-align: -4px;\"\/>k \\leq 2$ it is advisable to use the<br \/>\npreliminary test estimator (PTE).<\/p>\n<p><a href=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/uploads\/39n2_3.pdf\">Fulltext<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The problem of estimating the parameters of logistic regression model is considered when it is known from extraneous sources that the H_0:\\beta_0=\\ldots, =\\beta_{k-1}=\\beta^0k\u00a0 \\geq 3k \\leq 2$ it is advisable to use the preliminary test estimator (PTE). Fulltext<\/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":[21],"issuem_issue_categories":[],"issuem_issue_tags":[],"class_list":["post-654","article","type-article","status-publish","format-standard","hentry","issuem_issue-v39n2"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Some improved estimators in logistic regression model - 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\/improved-estimators-logistic-regression-model\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Some improved estimators in logistic regression model - JSR\" \/>\n<meta property=\"og:description\" content=\"The problem of estimating the parameters of logistic regression model is considered when it is known from extraneous sources that the H_0:beta_0=ldots, =beta_{k-1}=beta^0k\u00a0 geq 3k leq 2$ it is advisable to use the preliminary test estimator (PTE). 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