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August 2020 Comment: Diagnostics and Kernel-based Extensions for Linear Mixed Effects Models with Endogenous Covariates
Hunyong Cho, Joshua P. Zitovsky, Xinyi Li, Minxin Lu, Kushal Shah, John Sperger, Matthew C. B. Tsilimigras, Michael R. Kosorok
Statist. Sci. 35(3): 396-399 (August 2020). DOI: 10.1214/20-STS782

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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Hunyong Cho. Joshua P. Zitovsky. Xinyi Li. Minxin Lu. Kushal Shah. John Sperger. Matthew C. B. Tsilimigras. Michael R. Kosorok. "Comment: Diagnostics and Kernel-based Extensions for Linear Mixed Effects Models with Endogenous Covariates." Statist. Sci. 35 (3) 396 - 399, August 2020. https://doi.org/10.1214/20-STS782

Information

Published: August 2020
First available in Project Euclid: 11 September 2020

MathSciNet: MR4148214
Digital Object Identifier: 10.1214/20-STS782

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.35 • No. 3 • August 2020
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