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Space-varying Regression Models: Specifications and Simulation

Author

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  • Dani Gamerman
  • Ajax R. B. Moreira
  • Havard Rue

Abstract

Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference pri- ors thus enabling incorporation of neighboring structures and easy sampling schemes. Different sampling schemes are available and may be used in an MCMC algorithm. These schemes are compared in terms of chain autocor- relation and resulting inference. We also discuss different prior specifications that accommodate the spatial structure. Results are illustrated with simulated data and applied to a real dataset. Os modelos de regressão com parâmetros variando no espaço são uma generalização dos modelos lineares em que é permitido aos coeficientes da regressão mudarem ao longo do espaço. A estrutura espacial é especificada por uma extensão multivariada de uma distribuição a priori que considera as diferenças entre os coeficientes de regiões vizinhas. Isso permite a incorporação da informação da vizinhança espacial. Para estimar o modelo utilizamos a abordagem bayesiana e o algoritmo do MCMC considerando diferentes esquemas de amostragem. Esses esquemas foram comparados em termos da autocorrelação da cadeia de Markov, e em termos dos resultados obtidos. Foram discutidas diferentes especificações a priori que admitem estruturas espaciais semelhantes. Os resultados são ilustrados com dados simulados e com um conjunto real de informações.

Suggested Citation

  • Dani Gamerman & Ajax R. B. Moreira & Havard Rue, 2015. "Space-varying Regression Models: Specifications and Simulation," Discussion Papers 0102, Instituto de Pesquisa Econômica Aplicada - IPEA.
  • Handle: RePEc:ipe:ipetds:0102
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