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Multinomial goodness-of-fit: Large-sample tests with survey design correction and exact tests for small samples

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  • Ben Jann

    ()
    (ETH Zurich)

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Abstract

I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports large-sample tests for complex survey designs and exact tests for small samples as well as classic large-sample Chi^2-approximation tests based on Pearson’s Chi^2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440 – 464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221 – 230) and par- allels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods or exhaustive enu- meration. mgof also provides an exact one-sample Kolmogorov-Smirnov test for discrete data. Copyright 2008 by StataCorp LP.

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Bibliographic Info

Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 8 (2008)
Issue (Month): 2 (June)
Pages: 147-169

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Handle: RePEc:tsj:stataj:v:8:y:2008:i:2:p:147-169

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Related research

Keywords: mgof; mgofi; multinomial; goodness-of-fit; chi-squared; cat- egorical data; exact tests; Monte Carlo; exhaustive enumeration; combinatorial algorithms; complex survey correction; power-divergence statistic; Kolmogorov-Smirnov; Benford's law;

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