[Invited tutorial] Birnbaum–Saunders regression models: a comparative evaluation of three approaches. Issue 14 (21st September 2020)

• Record Type: Journal Article
• Authors: Dasilva, Alan; Dias, Renata; Leiva, Victor; Marchant, Carolina; Saulo, Helton
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 14(2020)
• Page Start: 2552
• Page End: 2570
• Publication Date: 2020-09-21
• Subjects: Birnbaum–Saunders distributions -- maximum likelihood estimators -- Monte Carlo method -- residuals -- R software; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1782912
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : 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.