Local Block Bootstrap

Optimum Block Size in Separate Block Bootstrap to Estimate the Variance of Sample Mean for Lattice Data

The statistical analysis of spatial data is usually done under Gaussian assumption for the underlying random field model. When this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. One of the crucial problems in this setting is specifying the block sizes. In this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...

Local Block Bootstrap for Inhomogeneous Poisson Marked Point Processes

The asymptotic theory for the sample mean of a marked point process in d dimensions is established, allowing for the possibility that the underlying Poisson point process is inhomogeneous. A novel local block bootstrap method for resampling inhomogeneous Poisson marked point processes is introduced, and its consistency is proven for the sample mean and related statistics. Finite-sample simulati...

Technical supplement to : “ Local Block Bootstrap for Inhomogeneous Poisson Marked Point Processes ”

The Block - Block Bootstrap : Improved Asymptotic Refinements

1 The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join-point problem. In this paper, we propose a method of solving...

The Block–block Bootstrap: Improved Asymptotic Refinements by Donald

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the joinpoint problem. In this paper, we propose a method of solving th...