Penalized maximum likelihood estimation in the modified extended Weibull distribution |
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Authors: | Verônica M. C. Lima Francisco Cribari–Neto |
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Affiliation: | 1. Departamento de Estatística, Universidade Federal da Bahia, Campus Ondina, Salvador/BA, Brazil;2. Departamento de Estatística, Universidade Federal de Pernambuco, Cidade Universitária, Recife/PE, Brazil |
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Abstract: | We address the issue of performing inference on the parameters that index the modified extended Weibull (MEW) distribution. We show that numerical maximization of the MEW log-likelihood function can be problematic. It is even possible to encounter maximum likelihood estimates that are not finite, that is, it is possible to encounter monotonic likelihood functions. We consider different penalization schemes to improve maximum likelihood point estimation. A penalization scheme based on the Jeffreys’ invariant prior is shown to be particularly useful. Simulation results on point estimation, interval estimation, and hypothesis testing inference are presented. Two empirical applications are presented and discussed. |
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Keywords: | Jeffreys’ prior Maximum likelihood Modified extended Weibull distribution Monotone likelihood |
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