Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables
| Autor: | Jan Vogler, Roman Liesenfeld, Jean-François Richard |
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| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Gaussian 05 social sciences Covariance Poisson distribution 01 natural sciences Censoring (statistics) 010104 statistics & probability symbols.namesake 0502 economics and business symbols Spatial econometrics Truncation (statistics) 050207 economics 0101 mathematics Gaussian process Algorithm Importance sampling Mathematics |
| DOI: | 10.1108/s0731-905320160000037009 |
| Popis: | We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices. |
| Databáze: | OpenAIRE |
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