Plug‐in machine learning for partially linear mixed‐effects models with repeated measurements

Author:

Emmenegger Corinne1ORCID,Bühlmann Peter1

Affiliation:

1. Seminar for Statistics ETH Zurich Zurich Switzerland

Abstract

AbstractTraditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed‐effects model for repeated measurements. Using machine learning algorithms allows us to incorporate complex interaction structures, nonsmooth terms, and high‐dimensional variables. The linear variables and the response are adjusted nonparametrically for the nonlinear variables, and these adjusted variables satisfy a linear mixed‐effects model in which the linear coefficient can be estimated with standard linear mixed‐effects methods. We prove that the estimated fixed effects coefficient converges at the parametric rate, is asymptotically Gaussian distributed, and semiparametrically efficient. Two simulation studies demonstrate that our method outperforms a penalized regression spline approach in terms of coverage. We also illustrate our proposed approach on a longitudinal dataset with HIV‐infected individuals. Software code for our method is available in the R‐package dmlalg.

Funder

H2020 European Research Council

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sandwich boosting for accurate estimation in partially linear models for grouped data;Journal of the Royal Statistical Society Series B: Statistical Methodology;2024-05-09

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