Truncated Estimator of Asymptotic Covariance Matrix in Partially Linear Models with Heteroscedastic Errors  

作　　者：Yan-meng Zhao Jin-hong You Yong Zhou 

机构地区：[1]Department of Mathematics, Shenzhen University, Shenzhen 518060, China [2]Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7400, USA [3]Center for Statistical Research, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China

出　　处：《Acta Mathematicae Applicatae Sinica》2006年第4期565-574,共10页应用数学学报（英文版）

基　　金：Zhou's research was partially supported by the National Natural Science Foundation of China(No.10471140,10571169)

摘　　要：A partially linear regression model with heteroscedastic and/or serially correlated errors is studied here. It is well known that in order to apply the semiparametric least squares estimation （SLSE） to make statistical inference a consistent estimator of the asymptotic covariance matrix is needed. The traditional residual-based estimator of the asymptotic covariance matrix is not consistent when the errors are heteroscedastic and/or serially correlated. In this paper we propose a new estimator by truncating, which is an extension of the procedure in White. This estimator is shown to be consistent when the truncating parameter converges to infinity with some rate.A partially linear regression model with heteroscedastic and/or serially correlated errors is studied here. It is well known that in order to apply the semiparametric least squares estimation （SLSE） to make statistical inference a consistent estimator of the asymptotic covariance matrix is needed. The traditional residual-based estimator of the asymptotic covariance matrix is not consistent when the errors are heteroscedastic and/or serially correlated. In this paper we propose a new estimator by truncating, which is an extension of the procedure in White. This estimator is shown to be consistent when the truncating parameter converges to infinity with some rate.

关 键 词：Partially linear regression model heteroscedastic serially correlation semiparametric least squares estimation asymptotic covariance matrix 

分 类 号：O211.67[理学—概率论与数理统计]