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On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: the case of unknown parameters

Capasso, Marco, Alessi, Lucia, Barigozzi, Matteo and Fagiolo, Giorgio (2009) On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: the case of unknown parameters. Advances in Complex Systems, 12 (2). pp. 157-167. ISSN 0219-5259

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Abstract

This paper discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to re-estimate unknown parameters on each simulated Monte-Carlo sample — and thus avoiding to employ this information to build the test statistic — may lead to wrong, overly-conservative. Furthermore, we present some simple examples suggesting that the impact of this possible mistake may turn out to be dramatic and does not vanish as the sample size increases.

Item Type: Article
Official URL: http://www.worldscinet.com/acs/
Additional Information: © 2009 World Scientific Publishing
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 07 Jan 2011 10:45
URL: http://eprints.lse.ac.uk/31119/

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