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Gap bootstrap methods for massive data sets with an application to transportation engineering. (English) Zbl 1257.62051

Summary: We describe two bootstrap methods for massive data sets. Naive applications of common resampling methodology are often impractical for massive data sets due to the 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.

MSC:

62G09 Nonparametric statistical resampling methods
62P30 Applications of statistics in engineering and industry; control charts
90B06 Transportation, logistics and supply chain management
62H12 Estimation in multivariate analysis
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
65C60 Computational problems in statistics (MSC2010)
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References:

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