Cross-Validation for Correlated Data

• 成果类型: Article
• 署名作者: Rabinowicz, Assaf; Rosset, Saharon
• 署名单位: Tel Aviv University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1801451
• 发表日期: 2022
• 页码: 718-731
• 关键词: inference; models; error

摘要

K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions, in particular in cases involving non-iid data. This article analyzes CV for correlated data. We present a criterion for suitability of standard CV in presence of correlations. When this criterion does not hold, we introduce a bias corrected CV estimator which we termthat yields an unbiased estimate of prediction error in many settings where standard CV is invalid. We also demonstrate our results numerically, and find that introducing our correction substantially improves both, model evaluation and model selection in simulations and real data studies.for this article are available online.