An asymptotic theory for the nugget estimator in spatial models

DOI10.1080/10485250903193997zbMath1182.62186OpenAlexW2023475047MaRDI QIDQ5189268

Tae Yoon Kim, Gyu Moon Song, Jeong-Soo Park

Publication date: 15 March 2010

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250903193997

zbMATH Keywords

rate of convergencecentral limit theoremnearly infill domain samplingrisk calculationnonparametric nugget estimator

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05)

Related Items (1)

On nonparametric variogram estimation

Cites Work

• Spatial smoothing, nugget effect and infill asymptotics
• Practically applicable central limit theorem for spatial statistics
• Optimal bandwidth selection in nonparametric regression function estimation
• Spatial designs and properties of spatial correlation: effects on covariance estimation
• Statistical Methods for Spatial Data Analysis
• Statistical Meta‐Analysis with Applications
• Towards reconciling two asymptotic frameworks in spatial statistics
• Optimal Geostatistical Model Selection

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