{"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