Fitting spatial regressions to large datasets using unilateral approximations
| Autor: | Giuseppe Espa, Giuseppe Arbia, Flavio Santi, Marco Bee |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Settore SECS-S/03 - STATISTICA ECONOMICA spatial regression Maximum likelihood unilateral processes Monte Carlo method computer.software_genre 01 natural sciences approximate estimation gaussian process regular lattice spatial regression unilateral process very large dataset 010104 statistics & probability symbols.namesake Spatial model Consistency (statistics) 0502 economics and business unilateral process 0101 mathematics Gaussian process 050205 econometrics Mathematics 05 social sciences Estimator Regular lattice approximate estimation very large dataset regular lattice Spatial regression symbols gaussian process Data mining Algorithm computer |
| Zdroj: | Communications in Statistics - Theory and Methods. 47:222-238 |
| ISSN: | 1532-415X 0361-0926 |
| DOI: | 10.1080/03610926.2017.1301476 |
| Popis: | 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 model estimation (suggested in Besag 1974) provides a viable alternative to maximum likelihood estimation that reduces substantially the computing time and the storage required. In this article, we extend the method, originally proposed for conditionally specified processes, to simultaneous and to general bilateral spatial processes over rectangular lattices. We prove the estimators’ consistency and study their finite-sample properties via Monte Carlo simulations. |
| Databáze: | OpenAIRE |
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