VARIABLE SELECTION BY PSEUDO WAVELETS IN HETEROSCEDASTIC REGRESSION MODELS INVOLVING TIME SERIES  

作　　者：王清河 周勇 

机构地区：[1]Department of Applied Mathematics University of Petroleum,Shandong 257062,China [2]Institute of Applied Mathematics Academy of Mathematics and Systems Science The Chinese Academy of Sciences,Beijing 100080,China

出　　处：《Acta Mathematica Scientia》2006年第3期469-476,共8页数学物理学报（B辑英文版）

基　　金：Zhou's research was partially supported by the foundations of NatioiMd Natural Science (10471140) and (10571169) of China.

摘　　要：A simple but efficient method has been proposed to select variables in heteroscedastic regression models. It is shown that the pseudo empirical wavelet coefficients corresponding to the significant explanatory variables in the regression models are clearly larger than those nonsignificant ones, on the basis of which a procedure is developed to select variables in regression models. The coefficients of the models are also estimated. All estimators are proved to be consistent.A simple but efficient method has been proposed to select variables in heteroscedastic regression models. It is shown that the pseudo empirical wavelet coefficients corresponding to the significant explanatory variables in the regression models are clearly larger than those nonsignificant ones, on the basis of which a procedure is developed to select variables in regression models. The coefficients of the models are also estimated. All estimators are proved to be consistent.

关 键 词：Heteroscedastic regression models variable selection WAVELETS 

分 类 号：O211[理学—概率论与数理统计]