Gap bootstrap methods for massive data sets with an application to transportation engineering

S. N. Lahiri, C. Spiegelman, J. Appiah, L. Rilett

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In this paper we describe two bootstrap methods for massive data sets. Naive applications of common resampling methodology are often impractical for massive data sets due to computational burden and due to complex patterns of inhomogeneity. In contrast, the proposed methods exploit certain structural properties of a large class of massive data sets to break up the original problem into a set of simpler subproblems, solve each subproblem separately where the data exhibit approximate uniformity and where computational complexity can be reduced to a manageable level, and then combine the results through certain analytical considerations. The validity of the proposed methods is proved and their finite sample properties are studied through a moderately large simulation study. The methodology is illustrated with a real data example from Transportation Engineering, which motivated the development of the proposed methods.

Original languageEnglish (US)
Pages (from-to)1552-1587
Number of pages36
JournalAnnals of Applied Statistics
Issue number4
StatePublished - 2012


  • Exchangeability
  • Multivariate time series
  • Nonstationarity
  • OD matrix estimation
  • OD split proportion
  • Resampling methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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