Resampling Methods for Spatial Data


Jun Zhu
Department of Statistics
University of Wisconsin-Madison

Wednesday, 6 October 2004
3:10 PM
E202, Engineering

ABSTRACT
Under a large-sample scheme that is a mixture of the infill and increasing domain asymptotics, a functional central limit theorem is established for the empirical processes of a stationary strong-mixing random field. Further, a block bootstrap of the samples is applied and under suitable conditions, the bootstrapped empirical process is shown to converge weakly to the same limiting Gaussian process almost surely. Extensions to multivariate random fields and irregularly spaced sampling design are also considered.

Refreshments will be served at 2:45 p.m. in Room 008 of the Statistics Building



 

 

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