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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

By Marco Capasso, Lucia Alessi, Matteo Barigozzi and Giorgio Fagiolo

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

Topics: HA Statistics
Publisher: World scientific publishing
Year: 2009
DOI identifier: 10.1142/S0219525909002131
OAI identifier: oai:eprints.lse.ac.uk:31119
Provided by: LSE Research Online
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