Large sample theory of the estimation of the error distribution for a semiparametric model

被引：13
|
作者：
Liang, H
Härdle, W
机构：
[1] Chinese Acad Sci, Inst Syst Sci, Beijing 100080, Peoples R China
[2] Humboldt Univ, Inst Stat & Okonometrie, D-10178 Berlin, Germany
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 1999年 / 28卷 / 09期
基金：
中国国家自然科学基金;
关键词：
weak; strong consistency; uniformly strong consistency; rates of convergence; asymptotic normality; law of the iterated logarithm; semiparametric model;
D O I：
10.1080/03610929908832403
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 paper studies large sample theory of estimators of the error distribution for the semiparametric model Y = X(T)beta + g(T) + epsilon. Under appropriate conditions, we prove that the estimators converge in probability, converge almost surely and converge uniformly almost surely. Asymptotic normality and the rates of convergence of the estimators are also investigated. Finally we establish the law of the iterated logarithm for the estimators.
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页码：2025 / 2036
页数：12
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