On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters
Data Analysis, Statistics and Probability
2008-04-01 v1
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 a simple example suggesting that the impact of this possible mistake may turn out to be dramatic and does not vanish as the sample size increases.
Keywords
Cite
@article{arxiv.0803.4322,
title = {On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters},
author = {Marco Capasso and Lucia Alessi and Matteo Barigozzi and Giorgio Fagiolo},
journal= {arXiv preprint arXiv:0803.4322},
year = {2008}
}
Comments
11 pages, 1 table, 3 figures (4 boxes)