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 testing. 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.

On Approximating the Distributions of Goodness-of-fit Test Statistics Based on the Empirical Distribution Function: The Case of Unknown Parameters

BARIGOZZI M;
2009

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 testing. 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.
CAPASSO M; ALESSI L; BARIGOZZI M; FAGIOLO G
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/722365
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