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Bootstrap is commonly used as a tool for non-parametric statistical inferenceto estimate meaningful parameters in Variable Selection Models. However, formassive dataset that has exponential growth rate, the computation of BootstrapVariable Selection (BootVS) can be a crucial issue. In this paper, we proposethe method of Variable Selection with Bag of Little Bootstraps (BLBVS) onGeneral Linear Regression and extend it to Generalized Linear Model forselecting 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 anddistributed computing structures. To test the performance of BLBVS, we compareit with BootVS from different aspects via empirical studies. The results ofsimulations show our method has excellent performance. A real data analysis,Risk Forecast of Credit Cards, is also presented to illustrate thecomputational superiority of BLBVS on large scale datasets, and the resultdemonstrates the usefulness and validity of our proposed method.

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