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 (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 SYSTEM, 12, 157-167 [10.1142/S0219525909002131].
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
