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
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.
|Additional Information:||© 2009 World Scientific Publishing|
|Uncontrolled Keywords:||empirical distribution, statistics|
|Library of Congress subject classification:||H Social Sciences > HA Statistics|
|Sets:||Departments > Statistics|
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