| dc.creator | Dasilva, Alan | |
| dc.creator | Dias, Renata | |
| dc.creator | Leiva, Víctor | |
| dc.creator | Marchant-Fuentes, Carolina | |
| dc.creator | Saulo, Helton | |
| dc.date | 2020-11-12T13:37:43Z | |
| dc.date | 2020-11-12T13:37:43Z | |
| dc.date | 2020 | |
| dc.date.accessioned | 2022-10-18T12:13:07Z | |
| dc.date.available | 2022-10-18T12:13:07Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/3185 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4443440 | |
| dc.description | This study investigates three regression models based on the Birnbaum–Saunders distribution. The first model is obtained directly through the Birnbaum–Saunders distribution; the second model is obtained via a logarithmic transformation in the response variable; and the third model employs a mean parametrization of this distribution. The primary objective of this study is to compare the performance of the three Birnbaum–Saunders regression models. The secondary objective is to provide a tool to choose the best model for regression when analysing data following a Birnbaum–Saunders distribution. By using Monte Carlo simulations and the R software, we evaluate the behaviour of the corresponding estimators, and of the Cox–Snell and randomized quantile residuals. An illustration with real data is provided to compare the investigated regression models. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | Journal of Statistical Computation and Simulation, 90(14), 2552-2570 | |
| dc.subject | Birnbaum–Saunders distributions | |
| dc.subject | Maximum likelihood estimators | |
| dc.subject | Monte Carlo method | |
| dc.subject | Residuals | |
| dc.subject | R software | |
| dc.title | Birnbaum–Saunders regression models: a comparative evaluation of three approaches | |
| dc.type | Artículos de revistas | |
