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Fisher Information Test of Normality (1998)
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BibTeX
@TECHREPORT{Lee98fisherinformation,
author = {Yew-haur Lee and Yew-haur Lee},
title = {Fisher Information Test of Normality},
institution = {},
year = {1998}
}
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Abstract
An extremal property of normal distributions is that they have the smallest Fisher Information for location among all distributions with the same variance. A new test of normality proposed by Terrell (1995) utilizes the above property by finding that density of maximum likelihood constrained on having the expected Fisher Information under normality based on the sample variance. The test statistic is then constructed as a ratio of the resulting likelihood against that of normality. Since the asymptotic distribution of this test statistic is not available, the critical values for n = 3 to 200 have been obtained by simulation and smoothed using polynomials. An extensive power study shows that the test has superior power against distributions that are symmetric and leptokurtic (long-tailed). Another advantage of the test over existing ones is the direct depiction of any deviation from normality in the form of a density estimate. This is evident when the test is applied to several real data sets. Testing of normality in residuals is also investigated. Various approaches in dealing with residuals being possibly heteroscedastic and correlated suffer from a loss of power. The approach with the fewest undesirable features is to use the Ordinary Least
Keyphrases
fisher information test fisher information test statistic various approach density estimate sample variance direct depiction several real data set superior power extremal property undesirable feature critical value normal distribution new test maximum likelihood ordinary least asymptotic distribution extensive power study correlated suffer
