Variable Selection for Linear Regression Model with General Variance - Lenghoe, Nuchjaree; Suntornchost, Jiraphan (Journal of Applied Statistics and Information Technology, 2018)
Lahiri and Suntornchost (2015) proposed adjustments to variable selection criteria for the Fay-Herriot model by considering sampling errors. Their results were shown to improve traditional variable selection criteria. In this study, we extend their method to construct variable selection criteria for linear regression models with general variance assumption allowing for the possibility of correlated regression errors. Closed forms of statistics for variable selection criteria are provided with numerical studies. Simulation results show that our proposed variable selection criteria can reduce the approximation errors of the standard variable selection criterion.