Hypothesis testing in log-Birnbaum-Saunders regressions. Issue 5 (28th May 2017)

• Record Type: Journal Article
• Authors: Santos, Jéssica; Cribari-Neto, Francisco
• Is Part Of: Communications in statistics. Volume 46:Issue 5(2017)
• Page Start: 3990
• Page End: 4003
• Publication Date: 2017-05-28
• Subjects: Bootstrap -- Bootstrap Bartlett correction -- Likelihood ratio test -- Log-Birnbaum-Saunders model -- Monte Carlo simulation -- RESET test; Primary 62F03, 62J02 -- Secondary 65C05, 62F40; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1080834
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: The log-Birnbaum-Saunders regression model introduced by Rieck and Nedelman (1991 ) is useful for modeling lifetimes of materials and equipments subject to different conditions. Our goal in this article is twofold. First, we numerically evaluate the finite sample performances of the likelihood ratio, score and Wald tests in the log-Birnbaum-Saunders regression model. Second, we introduce a RESET-like misspecification test for that model. The null hypothesis is that the model is correctly specified which is tested against the alternative hypothesis of model misspecification. The power of the test is evaluated using Monte Carlo simulations. Bootstrap-based inference is also considered. An empirical application is presented and discussed.