A mixed effects log-linear model based on the Birnbaum–Saunders distribution

A.F. Desmond, Carlos L. Cíntora González, R.S. Singh and Xuewen Lu

Computational Statistics & Data Analysis, 2012, vol. 56, issue 2, 399-407

Abstract: In lifetime data analysis and particularly in engineering reliability contexts, the Birnbaum–Saunders (BISA) density is often suggested as a suitable model; see Birnbaum and Saunders (1969), Mann et al. (1974), and Desmond (1985). A linear regression model, obtained from a logarithmic transformation of the response variable, is useful in studying the effect of covariates on the response variable; see Rieck and Nedelman (1991), Tsionas (2001) and Galea et al. (2004). In this paper, an extension of the log-linear regression model of Rieck and Nedelman (1991), which considers random effects, is introduced. From a Monte Carlo simulation study, the performance of various estimation and prediction methods are studied. The usefulness of the mixed log-linear model is stressed and compared to the pure fixed effects log-linear regression BISA model. The new model is used to analyze a real data set, for which a fixed effects model is inappropriate.

Keywords: Birnbaum–Saunders distribution; Log-linear; Mixed effects; Regression; Reliability (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:2:p:399-407

DOI: 10.1016/j.csda.2011.07.017

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