论文标题
半连续纵向数据的两部分有限混合物回归模型
A two-part finite mixture quantile regression model for semi-continuous longitudinal data
论文作者
论文摘要
本文为半连续纵向数据开发了两部分有限混合物回归模型。提出的方法允许影响二进制响应变量模型的异质性源，也会影响阳性结果的分布。正如分数回归文献中常见的那样，对模型参数的估计和推断是基于不对称的拉普拉斯分布。最大似然估计是通过EM算法获得的，而没有参数假设对随机效应分布。此外，提出了EM算法的惩罚版本，以解决可变选择的问题。提出的统计方法应用于著名的RAND健康保险实验数据集，该数据集对其经验行为提供了进一步的见解。
This paper develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable, to influence also the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the Asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behavior.
