An asymptotically unbiased weighted least squares estimation criterion for parametric variograms of second order stationary geostatistical processes

DOI10.1080/03610918.2018.1508698OpenAlexW2900119214WikidataQ128989837 ScholiaQ128989837MaRDI QIDQ5086322

Sourav Das, Georgi N. Boshnakov, T. Subba Rao

Publication date: 5 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://www.research.manchester.ac.uk/portal/en/publications/an-asymptotically-unbiased-weighted-least-squares-estimation-criterion-for-parametric-variograms-of-second-order-stationary-geostatistical-processes(253cf096-9f0a-4305-9352-dc6eec600a6e).html

zbMATH Keywords

variance stabilizationMatérn classcross-validation krigingwave variogram

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• nlme

Cites Work

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• Model-based geostatistics.
• Spatial variation. 2nd ed
• Time series: theory and methods.
• Interpolation of spatial data. Some theory for kriging
• On asymptotic distribution and asymptotic efficiency of least squares estimators of spatial variogram parameters
• Statistical Methods for Spatial Data Analysis
• Model-Based Geostatistics
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
• A New Covariance Function and Spatio‐Temporal Prediction (Kriging) for A Stationary Spatio‐Temporal Random Process

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