{"id":327,"date":"2017-05-09T10:39:15","date_gmt":"2017-05-09T10:39:15","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=327"},"modified":"2017-05-09T10:39:21","modified_gmt":"2017-05-09T10:39:21","slug":"using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/","title":{"rendered":"Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates"},"content":{"rendered":"

This paper considers the problem of missing data in a linear regression model.
\nIt presents a method to analyze and detect the missing completely at random
\n(MCAR) process when some values of covariates are missing but corresponding
\nvalues of response variable are available. The idea of using outlier detection
\nmethod in linear regression model is proposed to be employed to detect a non-
\nMCAR processes. Such an idea is utilized and a graphical method is proposed to
\nvisualize the problem.<\/p>\n

 <\/p>\n

44n2_3<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

This paper considers the problem of missing data in a linear regression model. It presents a method to analyze and detect the missing completely at random (MCAR) process when some values of covariates are missing but corresponding values of response variable are available. The idea of using outlier detection method in linear regression model is […]<\/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":[10],"issuem_issue_categories":[],"issuem_issue_tags":[],"class_list":["post-327","article","type-article","status-publish","format-standard","hentry","issuem_issue-v44n2"],"yoast_head":"\nUsing diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates - 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\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates - JSR\" \/>\n<meta property=\"og:description\" content=\"This paper considers the problem of missing data in a linear regression model. It presents a method to analyze and detect the missing completely at random (MCAR) process when some values of covariates are missing but corresponding values of response variable are available. The idea of using outlier detection method in linear regression model is […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jsr.isrt.ac.bd\/article\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/\" \/>\n<meta property=\"og:site_name\" content=\"JSR\" \/>\n<meta property=\"article:modified_time\" content=\"2017-05-09T10:39:21+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/\",\"url\":\"https:\/\/jsr.isrt.ac.bd\/article\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/\",\"name\":\"Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates - JSR\",\"isPartOf\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/#website\"},\"datePublished\":\"2017-05-09T10:39:15+00:00\",\"dateModified\":\"2017-05-09T10:39:21+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/jsr.isrt.ac.bd\/article\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/#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\":\"Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates\"}]},{\"@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":"Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates - 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\/using-diagnostic-measures-detect-non-mcar-processes-linear-regression-models-missing-covariates\/","og_locale":"en_US","og_type":"article","og_title":"Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates - JSR","og_description":"This paper considers the problem of missing data in a linear regression model. It presents a method to analyze and detect the missing completely at random (MCAR) process when some values of covariates are missing but corresponding values of response variable are available. 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