On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters
| dc.creator | Capasso, Marco | |
| dc.creator | Alessi, Lucia | |
| dc.creator | Barigozzi, Matteo | |
| dc.creator | Fagiolo, Giorgio | |
| dc.date | 2008-03-30 | |
| dc.date.accessioned | 2026-07-07T09:29:21Z | |
| dc.date.available | 2026-07-07T09:29:21Z | |
| dc.description | 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. | |
| dc.description | 11 pages, 1 table, 3 figures (4 boxes) | |
| dc.identifier | https://arxiv.org/abs/0803.4322 | |
| dc.identifier | http://arxiv.org/abs/0803.4322 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/157756 | |
| dc.subject | Data Analysis, Statistics and Probability | |
| dc.title | On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters | |
| dc.type | text |