Spatial sampling design using generalized Neyman-Scott process
type: Item
instance of: scholarly article
title: Spatial sampling design using generalized Neyman-Scott process
author: Sze Him Leung
author: Ji Meng Loh
author: Chun Yip Yau
author: Zhengyuan Zhu
publication date: 2022-10-18T00:00:00Z
Mathematics Subject Classification ID: 62P12
zbMATH DE Number: 7603056
zbMATH Keywords: cross-entropy method
zbMATH Keywords: geostatistics
zbMATH Keywords: kriging
zbMATH Keywords: Neyman-Scott process
zbMATH Keywords: Matérn covariance function
describes a project that uses: GenSA
describes a project that uses: CEoptim
MaRDI profile type: Publication
full work available at URL: s13253-020-00413-3
OpenAlex ID: W3087380167
cites work: Statistics for Spatial Data
cites work: Unsolved problems in geometry
cites work: Q4370586
cites work: Bayesian Geostatistical Design
cites work: Spatial designs and properties of spatial correlation: effects on covariance estimation
cites work: Generalised shot noise Cox processes
cites work: Stochastic Estimation of the Maximum of a Regression Function
cites work: Q3256936
cites work: On estimators for the reduced second moment measure of point processes
cites work: A controlled random search procedure for global optimisation
cites work: Asymptotic optimality of regular sequence designs
cites work: Optimization of computer simulation models with rare events
cites work: The cross-entropy method for combinatorial and continuous optimization
cites work: Sampling designs for estimation of a random process
cites work: A nonparametric measure of spatial interaction in point patterns
cites work: Q3145547
cites work: Spatial sampling design for parameter estimation of the covariance function
DOI: 10.1007/S13253-020-00413-3
published in: Journal of Agricultural, Biological and Environmental Statistics
description: scientific article; zbMATH DE number 7603056