On a length-biased Birnbaum-Saunders regression model applied to meteorological data

Kessys L. P. Oliveira, Bruno S. Castro, Helton Saulo and Roberto Vila

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 19, 6916-6935

Abstract: The length-biased Birnbaum-Saunders distribution is both useful and practical for environmental sciences. In this paper, we initially derive some new properties for the length-biased Birnbaum-Saunders distribution, showing that one of its parameters is the mode and that it is bimodal. We then introduce a new regression model based on this distribution. We implement the maximum likelihood method for parameter estimation, approach interval estimation and consider three types of residuals. An elaborate Monte Carlo study is carried out for evaluating the performance of the likelihood-based estimates, the confidence intervals and the empirical distribution of the residuals. Finally, we illustrate the proposed regression model with the use of a real meteorological data set.

Date: 2023
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DOI: 10.1080/03610926.2022.2037642

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