ASYMPTOTIC THEORY FOR PARTLY LINEAR-MODELS

被引：78

作者：

GAO, JT ^{[1
]}

机构：

[1] UNIV AUCKLAND,DEPT STAT,AUCKLAND,NEW ZEALAND

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 1995年 / 24卷 / 08期

关键词：

ASYMPTOTIC NORMALITY; STRONG CONVERGENCE; LINEAR PROCESS; PARTLY LINEAR MODEL;

D O I：

10.1080/03610929508831598

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：

Consider the model Y-i = x(i)(')beta + g(t(i)) + V-i, 1 less than or equal to i less than or equal to n. Here x(i)=(x(il),...,x(ip))' and t(i) are known and nonrandom design points, beta=(beta(1),...,beta(p)) is an unknown parameter, g(.) is an unknown function over R(1), and V-i is a class of linear processes. Based on g(.) estimated by nonparametric kernel estimation or approximated by a finite series expansion, the asymptotic normalities and the strong consistencies of the LS estimator of beta and an estimator of sigma(0)(2)=EV(1)(2) are investigated.

引用

页码：1985 / 2009

页数：25

共 21 条

[1] ASYMPTOTIC NORMALITY OF SERIES ESTIMATORS FOR NONPARAMETRIC AND SEMIPARAMETRIC REGRESSION-MODELS [J].