Iterative weighted least-squares estimates in a heteroscedastic linear regression model

被引：4

作者：

Inoue, K ^{[1
]}

机构：

[1] Fukushima Univ, Fac Econ, Dept Econ, Fukushima 9601296, Japan

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2003年 / 110卷 / 1-2期

关键词：

heteroscedastic linear regression; iterative procedure; replication; asymptotic variance; common mean; Graybill-Deal estimate; spherical distribution;

D O I：

10.1016/S0378-3758(01)00285-3

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The aim of this study is to improve the efficiency of weighted least-squares estimates for a regression parameter. An iterative procedure, starting with an unbiased estimate other than the unweighted least-squares estimate, yields estimates which are asymptotically more efficient than the feasible generalized least-squares estimate when errors are spherically distributed. The result has an application in the improvement of the Graybill-Deal estimate of the common mean of several normal populations, (C) 2001 Elsevier Science B.V. All rights reserved.

引用

页码：133 / 146

页数：14

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