Open Access
December 2012 Gap bootstrap methods for massive data sets with an application to transportation engineering
S. N. Lahiri, C. Spiegelman, J. Appiah, L. Rilett
Ann. Appl. Stat. 6(4): 1552-1587 (December 2012). DOI: 10.1214/12-AOAS587


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.


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S. N. Lahiri. C. Spiegelman. J. Appiah. L. Rilett. "Gap bootstrap methods for massive data sets with an application to transportation engineering." Ann. Appl. Stat. 6 (4) 1552 - 1587, December 2012.


Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62051
MathSciNet: MR3058675
Digital Object Identifier: 10.1214/12-AOAS587

Keywords: exchangeability , multivariate time series , nonstationarity , OD matrix estimation , OD split proportion , Resampling methods

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 4 • December 2012
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