当前位置: X-MOL 学术Stat. Model. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach
Statistical Modelling ( IF 1 ) Pub Date : 2021-04-07 , DOI: 10.1177/1471082x21993603
Luca Merlo 1 , Antonello Maruotti 2, 3 , Lea Petrella 4
Affiliation  

This article 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 also influence 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 behaviour.



中文翻译:

半连续纵向数据的两部分分位数回归模型:有限混合方法

本文针对半连续纵向数据开发了一个由两部分组成的有限混合分位数回归模型。所提出的方法允许影响二元响应变量模型的异质性源也影响阳性结果的分布。在分位数回归文献中很常见,模型参数的估计和推论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,无需对随机效应分布进行参数假设。此外,提出了EM算法的惩罚版本,以解决变量选择的问题。所提出的统计方法被应用于著名的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。

更新日期:2021-04-08
down
wechat
bug