Comment: Diagnostics and Kernel-based Extensions for Linear Mixed Effects Models with Endogenous Covariates

IF 3.9 1区数学 Q1 STATISTICS & PROBABILITY

Statistical Science Pub Date : 2020-08-01 DOI:10.1214/20-sts782

Hunyong Cho, Joshua P. Zitovsky, Xinyi Li, Minxin Lu, K. Shah, John Sperger, Matthew C. B. Tsilimigras, M. Kosorok

引用次数: 0

Abstract

We discuss “Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study” by Qian, Klasnja and Murphy. In this discussion, we study when the linear mixed effects models with endogenous covariates are feasible to use by providing examples and diagnostic tools as well as discussing potential extensions. This includes evaluating feasibility of partial likelihood-based inference, checking the conditional independence assumption, estimation of marginal effects, and kernel extensions of the model.

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评论：具有内生协变量的线性混合效应模型的诊断和基于核的扩展

我们讨论了钱、Klasnja和Murphy的“具有内生协变量的线性混合模型：序列治疗效果建模及其在移动健康研究中的应用”。在这场讨论中，我们通过提供例子和诊断工具以及讨论潜在的扩展，研究了具有内生协变量的线性混合效应模型何时可行。这包括评估基于偏似然推理的可行性，检查条件独立性假设，估计边际效应，以及模型的核扩展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Science 数学-统计学与概率论

CiteScore

6.50

自引率

1.80%

发文量

审稿时长

>12 weeks

期刊介绍： The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.