Bayesian Geophysical, Spatial and Temporal Statistics

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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Ming-Hui Chen: University of Connecticut, Department of Statistics
Dipak K. Dey: University of Connecticut, Department of Statistics
Peter Müller: The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics
Dongchu Sun: University of Missouri-Columbia, Department of Statistics
Keying Ye: University of Texas at San Antonio, Department of Management Science and Statistics, College of Business

Chapter Chapter 13 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 467-511 from Springer

Abstract: Abstract Spatio-temporal models give rise to many challenging research frontiers in Bayesian analysis. One simple reason is that the spatial and/or time series nature of the data implies complicated dependence structures that require modeling and lead to often challenging inference problems. The power of the Bayesian approach comes to bear especially when inference is desired on aspects of the model that are removed from the data by various levels in the hierarchical model. In this chapter we discuss two examples of such problems and also review the use of non-informative priors in spatial models.

Keywords: Generalize Extreme Value; Central Business District; Deviance Information Criterion; Generalize Extreme Value Distribution; Gaussian Random Field (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4419-6944-6_13

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DOI: 10.1007/978-1-4419-6944-6_13

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