JSM 2004 - Toronto

Abstract #301957

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Activity Number: 376
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #301957
Title: Testing and Detecting Non-normalities
Author(s): Toshinari Kamakura*+
Companies: Chuo University
Address: 1-13-27 Kasuga, Tokyo, International, 112-8551, Japan
Keywords: normality ; testing ; skewness ; kurtosis ; changepoint
Abstract:

Many statistical models are highly dependent on assumption of normal distribution. However, practicioners sometimes use the statistical model based on the normal assumption without any care of goodness of fitting observed data, so it is important to testing the normality. We will investigate the several methods of testing for normality of observations such as skewness measure test, D'Agostino test, Shapiro & Wilk test, and LM test based on simulation studies. The LM test may have good power over Pearson family, but it is still insufficient for detecting non-normality in case of structure change like change point problems. We will propose modified LM test and investigate its property of testing and detecting non-normality against not only Pearson family but also other group of distribution family used in the field of survival analysis.

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