Variable Selection with Scalable Bootstrap in Generalized Linear Model for Massive Data

TL;DR

This paper introduces BLBVS, a scalable bootstrap method for variable selection in generalized linear models, optimized for massive datasets with parallel computing capabilities.

Contribution

It extends the Bag of Little Bootstraps approach to generalized linear models, improving computational efficiency for large-scale data analysis.

Findings

BLBVS outperforms traditional bootstrap in speed and accuracy on large datasets.

Simulation studies confirm the method's excellent performance.

Real data application demonstrates BLBVS's computational advantages and validity.

Abstract

Bootstrap is commonly used as a tool for non-parametric statistical inference to estimate meaningful parameters in Variable Selection Models. However, for massive dataset that has exponential growth rate, the computation of Bootstrap Variable Selection (BootVS) can be a crucial issue. In this paper, we propose the method of Variable Selection with Bag of Little Bootstraps (BLBVS) on General Linear Regression and extend it to Generalized Linear Model for selecting important parameters and assessing the quality of estimators' computation efficiency by analyzing results of multiple bootstrap sub-samples. The introduced method best suits large datasets which have parallel and distributed computing structures. To test the performance of BLBVS, we compare it with BootVS from different aspects via empirical studies. The results of simulations show our method has excellent performance. A real data analysis, Risk Forecast of Credit Cards, is also presented to illustrate the computational superiority of BLBVS on large scale datasets, and the result demonstrates the usefulness and validity of our proposed method.