(Article of periodic en Anglais - 2013)

Document title

Computing the Jacobian in Gaussian spatial autoregressive models : an illustrated comparison of available methods

Authors(s) and Affiliation(s)



When estimating spatial regression models by maximum likelihood using spatial weights matrices to represent spatial processes, computing the Jacobian remains a central problem. In principle, and for smaller data sets, the use of the eigenvalues of the spatial weights matrix provides a very rapid resolution. Analytical eigenvalues are available for large regular grids. But for larger problems solving the eigenproblem may not be feasible, and a number of alternatives have been proposed. The article surveys selected alternatives, and comments on their relative usefulness. The results are presented in terms of component-wise differences between sets of Jacobians for selected data sets.


Article of periodic

published at : Geographical analysis / ISSN 0016-7363

Editor : Ohio State University Press, Columbus, OH - ETATS-UNIS (1969)

Millesime : 2013, vol. 45, no2 [pp. 150-179]

Bibliographic references : 2 p.

Collation : 4 fig., 17 tabl., équations



INIST-CNRS, Cote INIST : 17078

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.1111/gean.12008

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2013
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