{"id":361,"date":"2017-05-12T10:31:55","date_gmt":"2017-05-12T10:31:55","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=361"},"modified":"2017-05-12T10:32:33","modified_gmt":"2017-05-12T10:32:33","slug":"review-linear-mixed-models-small-area-estimation","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/review-linear-mixed-models-small-area-estimation\/","title":{"rendered":"A review of linear mixed models and small area estimation"},"content":{"rendered":"
The linear mixed models (LMM) and the empirical best linear unbiased predictor
\n(EBLUP) induced from LMM have been well studied and extensively used for a
\nlong time in many applications. Of these, EBLUP in small area estimation has
\nbeen recognized as a useful tool in various practical statistics. In this paper, we
\ngive a review on LMM and EBLUP from a aspect of small area estimation. Espe-
\ncially, we explain why EBLUP is likely to be reliable. The reason is that EBLUP
\npossesses the shrinkage function and the pooling e\u000bects as desirable properties,
\nwhich arise from the setup of random e\u000bects and common parameters in LMM.
\nSuch important properties of EBLUP are clari\fed as well as some recent results
\nof the mean squared error estimation, the con\fdence interval and the variable
\nselection procedures are summarized.<\/p>\n
<\/p>\n