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Improved likelihood inference in Birnbaum-Saunders regressions

Lemonte, Artur J.; Ferrari, Silvia L. P.; Cribari-Neto, Francisco Universidade De São Paulo

COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.54, n.5, p.1307-1316, 2010

ELSEVIER SCIENCE BV 2010

Acesso online

  • Título:
    Improved likelihood inference in Birnbaum-Saunders regressions
  • Autor: Lemonte, Artur J.; Ferrari, Silvia L. P.; Cribari-Neto, Francisco
  • Universidade De São Paulo
  • Assuntos: Influence Diagnostics; Models; Family; Computer Science; Interdisciplinary Applications; Statistics & Probability
  • É parte de: COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.54, n.5, p.1307-1316, 2010
  • Descrição: The Birnbaum-Saunders regression model is commonly used in reliability studies. We address the issue of performing inference in this class of models when the number of observations is small. Our simulation results suggest that the likelihood ratio test tends to be liberal when the sample size is small. We obtain a correction factor which reduces the size distortion of the test. Also, we consider a parametric bootstrap scheme to obtain improved critical values and improved p-values for the likelihood ratio test. The numerical results show that the modified tests are more reliable in finite samples than the usual likelihood ratio test. We also present an empirical application. (C) 2009 Elsevier B.V. All rights reserved.
    FAPESP
    CNPq (Brazil)
  • DOI: 10.1016/j.csda.2009.11.017
  • Títulos relacionados: Computational Statistics & Data Analysis
  • Editor: ELSEVIER SCIENCE BV
  • Data de publicação: 2010
  • Idioma: Inglês

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