A Monte Carlo simulation study on partially adaptive estimators of linear regression models

被引：8

作者：

Kantar, Yeliz Mert ^{[1
]}

Usta, Ilhan ^{[1
]}

Acitas, Sukru ^{[1
]}

机构：

[1] Anadolu Univ, Dept Stat, TR-26470 Eskisehir, Turkey

来源：

JOURNAL OF APPLIED STATISTICS | 2011年 / 38卷 / 08期

关键词：

linear regression model; non-normal error terms; partially adaptive estimator; sandwich estimator; Monte Carlo simulation; JACKKNIFE; ROBUST;

D O I：

10.1080/02664763.2010.516389

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：

This paper presents a comprehensive comparison of well-known partially adaptive estimators (PAEs) in terms of efficiency in estimating regression parameters. The aim is to identify the best estimators of regression parameters when error terms follow from normal, Laplace, Student's t, normal mixture, lognormal and gamma distribution via the Monte Carlo simulation. In the results of the simulation, efficient PAEs are determined in the case of symmetric leptokurtic and skewed leptokurtic regression error data. Additionally, these estimators are also compared in terms of regression applications. Regarding these applications, using certain standard error estimators, it is shown that PAEs can reduce the standard error of the slope parameter estimate relative to ordinary least squares.

引用

页码：1681 / 1699

页数：19

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