Least-squares fitting from the variogram cloud

DOI10.1016/S0167-7152(98)00250-8zbMath0915.62099MaRDI QIDQ1284592

Werner G. Müller

Publication date: 1999

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

zbMATH Keywords

spatial dependenceiterative reweighting

Mathematics Subject Classification ID

Applications of statistics (62P99) Survival analysis and censored data (62N99) Geostatistics (86A32)

Related Items (4)

Flexible and efficient estimating equations for variogram estimation ⋮ Aberrant Crypt Foci and Semiparametric Modeling of Correlated Binary Data ⋮ A regression approach for estimating the parameters of the covariance function of a stationary spatial random process ⋮ A comparison of sampling grids, cut-off distance and type of residuals in parametric variogram estimation

Uses Software

• GAUSS

Cites Work

• Quasi-likelihood and its application. A general approach to optimal parameter estimation
• The unifying role of iterative generalized least squares in statistical algorithms. With comments by Bent Jørgensen, Peter McCullagh, Joe R. Hill and a rejoinder by the author
• Performance robustness analysis of Kalman filter for linear discrete-time systems under plant and noise uncertainty
• Regression problems with controllable variables subject to error
• Variogram fitting by generalized least squares using an explicit formula for the covariance structure
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