Multifold Cross-Validation Model Averaging for Generalized Additive Partial Linear Models

نویسندگان

چکیده

Generalized Additive Partial Linear Models (GAPLMs) are appealing for model interpretation and prediction. However, GAPLMs, the covariates degree of smoothing in nonparametric parts often difficult to determine practice. To address this selection uncertainty issue, we develop a computationally feasible Model Averaging (MA) procedure. The weights data-driven selected based on multifold Cross-Validation (CV) (instead leave-one-out) computational saving. When all candidate models misspecified, show that proposed MA estimator GAPLMs is asymptotically optimal sense achieving lowest possible Kullback-Leibler loss. In other scenario where set contains at least one quasi-correct model, chosen by CV concentrated models. As by-product, propose variable importance measure quantify importances predictors weights. It shown be able identify variables true model. Moreover, when number very large, screening method provided. Numerical experiments superiority over some existing averaging methods. Supplementary materials article available online.

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ژورنال

عنوان ژورنال: Journal of Computational and Graphical Statistics

سال: 2023

ISSN: ['1061-8600', '1537-2715']

DOI: https://doi.org/10.1080/10618600.2023.2174127