2021/05/03 by Shishi Liu, Liu, Shishi, Jingxiao Zhang +1
Chemistry · Mathematics · #62G05 #62G08 #62R10 #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Methodology (stat.ME) #Spectroscopy and Chemometric Analyses #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2105.00966
openalex publication_date 2021/05/03 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28
In this paper, we propose a model averaging approach for addressing model uncertainty in the context of partial linear functional additive models. These models are designed to describe the relation between a response and mixed-types of predictors by incorporating both the parametric effect of scalar variables and the additive effect of a functional variable. The proposed model averaging scheme assigns weights to candidate models based on the minimization of a multi-fold cross-validation criterion. Furthermore, we establish the asymptotic optimality of the resulting estimator in terms of achieving the lowest possible square prediction error loss under model misspecification. Extensive simulation studies and an application to a near infrared spectra dataset are presented to support and illustrate our method.