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Nonparametric Likelihood Confidence Bands for a Distribution Function

Art B. Owen
Journal of the American Statistical Association
Vol. 90, No. 430 (Jun., 1995), pp. 516-521
DOI: 10.2307/2291062
Stable URL: http://www.jstor.org/stable/2291062
Page Count: 6
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Nonparametric Likelihood Confidence Bands for a Distribution Function
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Abstract

Berk and Jones described a nonparametric likelihood test of uniformity with greater asymptotic Bahadur efficiency than any weighted Kolmogorov-Smirnov test at any alternative to U[ 0, 1]. We invert this test to form confidence bands for a distribution function using Noe's recursion. Nonparametric likelihood bands are narrower in the tails and wider in the center than Kolmogorov-Smirnov bands and are asymmetric about the empirical cumulative distribution function. This article describes how to convert a confidence level into a likelihood threshold and how to use the threshold to compute bands. Simple, computation-saving approximations to the threshold are given for confidence levels 95% and 99% and all sample sizes up to 1,000. These yield coverage between the nominal and .01% over the nominal. The likelihood bands are illustrated on some galaxy velocity data and are shown to improve power over Kolmogorov-Smirnov bands on some examples with n = 20

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