Least Absolute Value vs. Least Squares Estimation and Inference Procedures in Regression Models with Asymmetric Error Distributions

被引：2

作者：

Dielman, Terry E. ^{[1
]}

机构：

[1] Texas Christian Univ, Decis Sci, MJ Neeley Sch Business, Ft Worth, TX 76129 USA

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2009年 / 8卷 / 01期

关键词：

L-1; regression; least absolute deviations; robust regression; simulation;

D O I：

10.22237/jmasm/1241136720

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：

A Monte Carlo simulation is used to compare estimation and inference procedures in least absolute value (LAV) and least squares (LS) regression models with asymmetric error distributions. Mean square errors (MSE) of coefficient estimates are used to assess the relative efficiency of the estimators. Hypothesis tests for coefficients are compared on the basis of empirical level of significance and power.

引用

页码：147 / 160

页数：14

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