Abstract
When testing non-nested models, the asymptotic distribution theory of the ordinary likelihood
ratio statistic is not valid anymore. Several test statistics, some of them based on
information criteria, have been proposed in order to test such non-nested hypotheses. Concerning
bootstrap approaches to simulate goodness-of-fit measures such as the likelihood ratio
value, have been elaborated as well. Based on these methods, we extend existing bootstrap
simulations towards out-of-sample bootstrap evaluation. As an application, a parametric
bootstrap on simulated regression data is provided.
ratio statistic is not valid anymore. Several test statistics, some of them based on
information criteria, have been proposed in order to test such non-nested hypotheses. Concerning
bootstrap approaches to simulate goodness-of-fit measures such as the likelihood ratio
value, have been elaborated as well. Based on these methods, we extend existing bootstrap
simulations towards out-of-sample bootstrap evaluation. As an application, a parametric
bootstrap on simulated regression data is provided.
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 22nd Workshop on Statistical Modelling |
| Herausgeber*innen | Joan del Castillo, Anna Espinal, Pedro Puig |
| Seiten | 427 - 430 |
| Publikationsstatus | Veröffentlicht - 1 Dez. 2007 |