Improved likelihood-based inference in Birnbaum–Saunders nonlinear regression models
We address the issue of performing testing inference in Birnbaum–Saunders nonlinear re- gression models when the sample size is small. The likelihood ratio, Wald and score statis- tics provide the basis for testing inference on the parameters in this class of models. We focus on the small-sample cas...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/25455 |
| Acceso en línea: | https://repositorio.ufrn.br/jspui/handle/123456789/25455 |
| Access Level: | acceso abierto |
| Palabra clave: | Bartlett-type correction Bootstrap Likelihood ratio statistic Score statistic Wald statistic |
| Sumario: | We address the issue of performing testing inference in Birnbaum–Saunders nonlinear re- gression models when the sample size is small. The likelihood ratio, Wald and score statis- tics provide the basis for testing inference on the parameters in this class of models. We focus on the small-sample case, where the reference chi-squared distribution gives a poor approximation to the true null distribution of these test statistics. We derive a general Bartlett-type correction in matrix notation for the score test, which reduces the size distor- tion of the test, and numerically compare the proposed test with the usual likelihood ratio, Wald and score tests, and with the Bartlett-corrected likelihood ratio test, and bootstrap- corrected tests. Our simulation results suggest that the proposed corrected test can be an interesting alternative to other tests since it leads to very accurate inference even for very small samples. We also present an empirical application for illustrative purposes. |
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