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

delete2020-08-01
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OA
AI
H
Hunyong Cho *
J
Joshua P. Zitovsky
X
Xinyi Li
M
Minxin Lu
K
Kushal S. Shah
J
John Sperger
M
Matthew C. B. Tsilimigras
M
Michael R. Kosorok
DOI:10.1214/20-STS782delete
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摘要

摘要

En 中文
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.
Keyword:
Linear mixed models
partial likelihood
conditional independence test
marginal effects
kernel mixed models

期刊

Statistical Science 封面图
Statistical Science
IF:
3.4
论文数:
1.0K
被引数:
8.7K

机构

U
university of north carolina
学者数:
7.4W
论文数: 6.5W
被引数: 93
U
University of North Carolina School of Medicine
学者数:
1.6W
论文数: 1.1W
被引数: 20
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