  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.
