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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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Author Info
Ben Jann ()

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Abstract

A new Stata command called -mgof- is introduced. The command is used to compute distributional tests for discrete (categorical, multinomial) variables. Apart from classic large sample $\chi^2$-approximation tests based on Pearson's $X^2$, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read 1984), large sample tests for complex survey designs and exact tests for small samples are supported. The complex survey correction is based on the approach by Rao and Scott (1981) and parallels the survey design correction used for independence tests in -svy:tabulate-. The exact tests are computed using Monte Carlo methods or exhaustive enumeration. An exact Kolmogorov-Smirnov test for discrete data is also provided.

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File URL: http://repec.ethz.ch/rsc/ets/wpaper/jann_mgof.pdf
File Format: application/pdf
File Function: First version, 2008
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Publisher Info
Paper provided by ETH Zurich, Chair of Sociology in its series ETH Zurich Sociology Working Papers with number 2.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 23 pages
Date of creation: Jan 2008
Date of revision:
Handle: RePEc:ets:wpaper:2

Contact details of provider:
Web page: http://www.socio.ethz.ch/

For technical questions regarding this item, or to correct its listing, contact: (Ben Jann).

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

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Econometric and Statistical Methods; Specific Distributions

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This page was last updated on 2009-11-5.


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