Fitting spatial regressions to large datasets using unilateral approximations

Giuseppe Arbia, Marco Bee, Giuseppe Espa, Flavio Santi

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

2 Citazioni (Scopus)

Abstract

Maximum likelihood estimation of a spatial model typically requires a sizeable computational capacity, even in relatively small samples, and becomes unfeasible in very large datasets. The unilateral approximation approach to spatial models estimation (suggested in Besag, 1974) provides a viable alternative to maximum likelihood estimation that reduces substantially computing time and the storage required. In this paper we extend the method, originally proposed for conditionally specified processes, to simultaneous and to general bilateral spatial processes. We prove the estimators’ consistency and studytheir finite-sample propertiesvia Monte Carlo simulations.
Lingua originaleEnglish
pagine (da-a)222-238
Numero di pagine17
RivistaCOMMUNICATIONS IN STATISTICS, THEORY AND METHODS
Volume2018
DOI
Stato di pubblicazionePubblicato - 2018

Keywords

  • unilateral processes

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