Local Block Bootstrap for Inhomogeneous Poisson Marked Point Processes

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

Fast Block Variance Estimation Procedures for Inhomogeneous Spatial Point Processes

We introduce two new variance estimation procedures by using non-overlapping and overlapping blocks, respectively. The non-overlapping block (NOB) estimator can be viewed as the limit of the thinned block bootstrap (TBB) estimator recently proposed in Guan and Loh (2007), by letting the number of thinned processes and bootstrap samples therein both increase to infinity. Compared to the latter, ...

A Thinned Block Bootstrap Variance Estimation Procedure for Inhomogeneous Spatial Point Patterns

When modeling inhomogeneous spatial point patterns, it is of interest to fit a parametric model for the first-order intensity function (FOIF) of the process in terms of some measured covariates. Estimates for the regression coefficients, say β̂, can be obtained by maximizing a Poisson maximum likelihood criterion. Little work has been done on the asymptotic distribution of β̂ except in some speci...

Bootstrapping a Spatial

We consider the problem of resampling or bootstrapping a point process to get con dence intervals for the reduced second moment function We propose a resampling scheme for spatial data which we call the marked point method This is a variant of the block of blocks bootstrap rst introduced by K unsch A simulation study with a Poisson a clustered and a regular point process on the unit square in R...

Poisson-Lindley INAR(1) Processes: Some Estimation and Forecasting Methods

This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predict...