Work

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

Year: 2012

Type: article

Abstract: 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.

Source: The Annals of Applied Statistics

DOI: 10.1214/12-aoas587

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

Institutions: University of Nebraska–Lincoln, Texas A&M University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Transportation