A Subsampled Double Bootstrap for Massive Data

Srijan Sengupta, Stanislav Volgushev, Xiaofeng Shao

Research output: Contribution to journalArticlepeer-review

Abstract

The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets that are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Recently, Kleiner and co-authors proposed a method called BLB (bag of little bootstraps) for massive data, which is more computationally scalable with little sacrifice of statistical accuracy. Building on BLB and the idea of fast double bootstrap, we propose a new resampling method, the subsampled double bootstrap, for both independent data and time series data. We establish consistency of the subsampled double bootstrap under mild conditions for both independent and dependent cases. Methodologically, the subsampled double bootstrap is superior to BLB in terms of running time, more sample coverage, and automatic implementation with less tuning parameters for a given time budget. Its advantage relative to BLB and bootstrap is also demonstrated in numerical simulations and a data illustration. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1222-1232
Number of pages11
JournalJournal of the American Statistical Association
Volume111
Issue number515
DOIs
StatePublished - Jul 2 2016

Keywords

  • Big data
  • Computational cost
  • Resampling
  • Subsampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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