Faster maximum likelihood estimation of very large spatial autoregressive models: an extension of the Smirnov–Anselin result

DOI10.1080/00949650410001650126zbMath1053.62106OpenAlexW1968099699MaRDI QIDQ4832413

Daniel A. Griffith

Publication date: 4 January 2005

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650410001650126

zbMATH Keywords

Jacobianspatial statisticsmaximum likelihoodspatial autocorrelationnormalizing constantauto-Gaussiangeoreferenced data

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Geostatistics (86A32)

Related Items (2)

Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression ⋮ Least-modules estimates for spatial autoregression coefficients

Cites Work

• Eigenfunction properties and approximations of selected incidence matrices employed in spatial analyses
• Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach.

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