The Minimum Density Power Divergence Estimation for the Lognormal Density

被引：2

作者：

Pak, Ro Jin ^{[1
]}

机构：

[1] Dankook Univ, Dept Stat & Informat, Yoing 448701, South Korea

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2014年 / 43卷 / 21期

关键词：

Maximum likelihood estimation; Lognormal distribution; Minimum distance estimation;

D O I：

10.1080/03610926.2012.737493

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：

In this article, we implement the minimum density power divergence estimation for estimating the parameters of the lognormal density. We compare the minimum density power divergence estimator (MDPDE) and the maximum likelihood estimator (MLE) in terms of robustness and asymptotic distribution. The simulations and an example indicate that the MDPDE is less biased than MLE and is as good as MLE in terms of the mean square error under various distributional situations.

引用

页码：4582 / 4588

页数：7

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