2024/06/28 by Jingxiang Pan, Pan, Jingxiang, Xiaohui Yuan +1
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2406.19691
openalex publication_date 2024/06/28 · openalex created_date 2024/07/02 · openalex updated_date 2026/07/28
As computer resources become increasingly limited, traditional statistical methods face challenges in analyzing massive data, especially in functional data analysis. To address this issue, subsampling offers a viable solution by significantly reducing computational requirements. This paper introduces a subsampling technique for composite quantile regression, designed for efficient application within the functional linear model on large datasets. We establish the asymptotic distribution of the subsampling estimator and introduce an optimal subsampling method based on the functional L-optimality criterion. Results from simulation studies and the real data analysis consistently demonstrate the superiority of the L-optimality criterion-based optimal subsampling method over the uniform subsampling approach.