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[Invited tutorial] Birnbaum–Saunders regression models: a comparative evaluation of three approaches
Journal of Statistical Computation and Simulation ( IF 1.1 ) Pub Date : 2020-08-06 , DOI: 10.1080/00949655.2020.1782912
Alan Dasilva 1 , Renata Dias 1 , Victor Leiva 2 , Carolina Marchant 3 , Helton Saulo 1
Affiliation  

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.

中文翻译:

【特邀教程】伯恩鲍姆-桑德斯回归模型:三种方法的对比评价

本研究调查了基于 Birnbaum-Saunders 分布的三个回归模型。第一个模型直接通过 Birnbaum-Saunders 分布获得;第二个模型是通过响应变量的对数变换获得的;第三个模型采用该分布的平均参数化。本研究的主要目的是比较三个 Birnbaum-Saunders 回归模型的性能。第二个目标是在分析遵循 Birnbaum-Saunders 分布的数据时提供一种工具来选择最佳回归模型。通过使用 Monte Carlo 模拟和 R 软件,我们评估了相应估计量以及 Cox-Snell 和随机分位数残差的行为。提供了带有真实数据的插图以比较所研究的回归模型。
更新日期:2020-08-06
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