{"id":343,"date":"2017-05-09T11:19:17","date_gmt":"2017-05-09T11:19:17","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=343"},"modified":"2017-05-09T11:19:30","modified_gmt":"2017-05-09T11:19:30","slug":"quasi-empirical-bayes-modeling-measurement-error-models-r-estimation-regression-parameters","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/quasi-empirical-bayes-modeling-measurement-error-models-r-estimation-regression-parameters\/","title":{"rendered":"Quasi-empirical Bayes modeling of measurement error models and R-estimation of the regression parameters"},"content":{"rendered":"
This paper deals with the R-estimation of the regression parameters of a mea-
\nsurement error model: and ,
\nBy combining the two sets of the information, an emaculate regression model is obtained
\nusing “quasi-empirical Bayes” estimates of the x_1, \\ldots, x_n$.
\nThe model produces consistent estimates of the attenuated slope and the inter-
\ncept parameters and applies to broad range of regression problems. Asymptotic
\nproperties of the R-estimators are provided based on the \\quasi-Bayes regression
\nmodel”. Some simulated results are presented as evidence of the performances of
\nthe estimators.<\/p>\n
<\/p>\n