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Journal of Data Science


Variable Selection with Scalable Bootstrapping in Generalized Linear Model for Massive Data
Volume 21, Issue 1 (2023), pp. 87–105
Pub. online: 7 July 2022      Type: Computing In Data Science      Open accessOpen Access
Received
20 February 2022
Accepted
26 May 2022
Published
7 July 2022

Abstract

Bootstrapping is commonly used as a tool for non-parametric statistical inference to assess the quality of estimators in variable selection models. However, for a massive dataset, the computational requirement when using bootstrapping in variable selection models (BootVS) can be crucial. In this study, we propose a novel framework using a bag of little bootstraps variable selection (BLBVS) method with a ridge hybrid procedure to assess the quality of estimators in generalized linear models with a regularized term, such as lasso and group lasso penalties. The proposed method can be easily and naturally implemented with distributed computing, and thus has significant computational advantages for massive datasets. The simulation results show that our novel BLBVS method performs excellently in both accuracy and efficiency when compared with BootVS. Real data analyses including regression on a bike sharing dataset and classification of a lending club dataset are presented to illustrate the computational superiority of BLBVS in large-scale datasets.

Supplementary material

 Supplementary Material
.zip contains the following files and/or directories: • /code and data/: Directory that includes code and files necessary to reproduce the numerical results presented in this paper. • supplementary.pdf: Online supplementary material.

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Copyright
2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
distributed computing large-scale dataset scalable bootstrap variable selection

Funding
Dr. Yang Li was supported by Platform of Public Health & Disease Control and Prevention, Major Innovation & Planning Interdisciplinary Platform for the “Double-First Class” Initiative, Renmin University of China and National Natural Science Foundation of China (71771211).

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