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arXiv · 2211.13839

Bivariate log-symmetric models: distributional properties, parameter estimation and an application to fatigue data analysis

Roberto Vila·Narayanaswamy Balakrishnan·Helton Saulo·Ana Protazio
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Abstract

The bivariate Gaussian distribution has been a key model for many developments in statistics. However, many real-world phenomena generate data that follow asymmetric distributions, and consequently bivariate normal model is inappropriate in such situations. Bidimensional log-symmetric models have attractive properties and can be considered as good alternatives in these cases. In this paper, we discuss bivariate log-symmetric distributions and their characterizations. We establish several distributional properties and obtain the maximum likelihood estimators of the model parameters. A Monte Carlo simulation study is performed for examining the performance of the developed parameter estimation method. A real data set is finally analyzed to illustrate the proposed model and the associated inferential method.

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Roberto Vila, Narayanaswamy Balakrishnan, Helton Saulo, Ana Protazio. 2022-11-25. Bivariate log-symmetric models: distributional properties, parameter estimation and an application to fatigue data analysis. https://arxiv.org/abs/2211.13839

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