Title

Simple and exact empirical likelihood ratio tests for normality based on moment relations

Document Type

Article

Publication Date

2010

Publication Title

Communications in Statistics - Simulation and Computation

Volume

40

Issue

1

First page number:

129

Last page number:

146

Abstract

The empirical likelihood (EL) technique is a powerful nonparametric method with wide theoretical and practical applications. In this article, we use the EL methodology in order to develop simple and efficient goodness-of-fit tests for normality based on the dependence between moments that characterizes normal distributions. The new empirical likelihood ratio (ELR) tests are exact and are shown to be very powerful decision rules based on small to moderate sample sizes. Asymptotic results related to the Type I error rates of the proposed tests are presented. We present a broad Monte Carlo comparison between different tests for normality, confirming the preference of the proposed method from a power perspective. A real data example is provided.

Keywords

Characterization theorems; Empirical likelihood; Goodness-of-fit; Goodness-of-fit tests; Omnibus test; Power study; Regression analysis; Statistics; Test for normality

Disciplines

Applied Statistics | Statistical Methodology | Statistical Models | Statistics and Probability | Vital and Health Statistics

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

Identifier

DOI: http://dx.doi.org/10.1080/03610918.2010.532896

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