Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/220191 
Erscheinungsjahr: 
2015
Schriftenreihe/Nr.: 
Discussion Paper No. 102
Verlag: 
Institute for Applied Economic Research (ipea), Brasília
Zusammenfassung: 
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 priors 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 autocorrelation 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.
Schlagwörter: 
Bayesian
Hyperparameters
Gibbs sampling
Markov chain Monte Carlo
Markov random fields
Metropolis-Hastings algorithm
Sampling schemes
Dokumentart: 
Working Paper

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