A skew–normal mixture of joint location, scale and skewness models
A skew–normal mixture of joint location, scale and skewness models
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DOI：
10.1007/s11766-016-3367-2
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Huiqiong Li;Liucang Wu;Jie-yi Yi
中科院分区：
文献类型：
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作者：
Huiqiong Li;Liucang Wu;Jie-yi Yi
正态混合回归模型是异质群体统计数据分析的重要工具之一。当考虑的数据集涉及不对称结果时，在过去的二十年中，在各种理论和应用问题中，偏态正态分布已被证明有利于处理不对称数据。本文提出并研究了一类新的模型：由联合位置、尺度和偏度模型组成的偏正态混合模型，用于分析来自异质种群的异方差偏正态数据。讨论了最大似然估计的问题。特别地，提出了一种估计模型参数的期望最大化算法。通过蒙特卡罗实验对回归系数估计量的性质进行了评价。本文给出了对身体质量指数（BMI）数据的真实数据集的分析结果。
Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models: a skew–normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew–normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation–Maximization (EM) algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index (BMI) data are presented.