论文标题
双变量对数对称模型：分布性能，参数估计和疲劳数据分析的应用
Bivariate log-symmetric models: distributional properties, parameter estimation and an application to fatigue data analysis
论文作者
论文摘要
双变量高斯分布一直是统计中许多发展的关键模型。但是，许多现实现象会产生遵循不对称分布的数据，因此在这种情况下，双变量正常模型是不合适的。二维对数对称模型具有吸引人的特性，在这些情况下可以被视为良好的替代品。在本文中，我们讨论了双变量对数分布及其特征。我们建立了几种分布属性，并获得模型参数的最大似然估计器。进行了一项蒙特卡洛模拟研究，以检查开发的参数估计方法的性能。最终分析了一个真实的数据集，以说明提出的模型和相关的推论方法。
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.
