Recent Developments in Bootstrap Methods for Dependent Data

Abstract

type=main xml:id=jtsa12109-abs-0001> We propose a new resampling method, the dependent random weighting, for both time series and random fields. The method is a generalization of the traditional random weighting in that the weights are made to be temporally or spatially dependent and are adaptive to the configuration of the data. Unlike the block-based bootstrap or subsampling methods, the dependent random weighting can be used for irregularly spaced time series and spatial data without any implementational difficulty. Consistency of the distribution approximation is shown for both equally and unequally spaced time series. Simulation studies illustrate the finite sample performance of the dependent random weighting in comparison with the existing counterparts for both one-dimensional and two-dimensional irregularly spaced data.