Cross-Validation in Penalized Linear Mixed Models: Addressing Common Implementation Pitfalls

Tabitha K Peter; Patrick J Breheny

doi:10.48550/arxiv.2503.14374

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Cross-Validation in Penalized Linear Mixed Models: Addressing Common Implementation Pitfalls

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Cross-Validation in Penalized Linear Mixed Models: Addressing Common Implementation Pitfalls

Tabitha K Peter and Patrick J Breheny

ArXiV.org

Cornell University

03/18/2025

DOI: 10.48550/arxiv.2503.14374

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https://doi.org/10.48550/arxiv.2503.14374View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

In this paper, we develop an implementation of cross-validation for penalized linear mixed models. While these models have been proposed for correlated high-dimensional data, the current literature implicitly assumes that tuning parameter selection procedures developed for independent data will also work well in this context. We argue that such naive assumptions make analysis prone to pitfalls, several of which we will describe. Here we present a correct implementation of cross-validation for penalized linear mixed models, addressing these common pitfalls. We support our methods with mathematical proof, simulation study, and real data analysis.

Statistics - Methodology

Details

Title: Subtitle: Cross-Validation in Penalized Linear Mixed Models: Addressing Common Implementation Pitfalls
Creators: Tabitha K Peter
Patrick J Breheny
Resource Type: Preprint
Publication Details: ArXiV.org
DOI: 10.48550/arxiv.2503.14374
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 03/18/2025
Academic Unit: Biostatistics
Record Identifier: 9984802012102771

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