Bayesian analysis of crossclassified spatial data with autocorrelation.

Bayesian analysis of crossclassified spatial data with autocorrelation. Research Abstract Details 

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Bayesian analysis of crossclassified spatial data with autocorrelation. Abstract Text:

L Sun,M K Clayton,

We address the development of methods for analyzing crossclassified categorical data that are spatially autocorrelated. We first extend the autologistic model to accommodate two variables. Two bivariate autologistic models are constructed, namely a two-step model and a symmetric model. Importance sampling is used to approximate the complex normalizing factors that arise in these models, and Markov chain Monte Carlo techniques are used to generate simulations of posterior distributions. The resulting models then are expanded to accommodate trend surfaces and directional effects. Simulation studies and real data are used to illustrate this method.

Bayesian analysis of crossclassified spatial data with autocorrelation. Publishing Authors By Initials

L Sun,MK Clayton,

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Bayesian analysis of crossclassified spatial data with autocorrelation. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Biometrics

VOLUME: 64

Page Numbers: 74-84

Journal Abbreviation: Biometrics

ISSN: 0006-341X

DAY: 3

MONTH: 08

YEAR: 2007

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LANGUAGE: eng

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Grant and Affiliation Information for Bayesian analysis of crossclassified spatial data with autocorrelation.

AFFILIATION: Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, Wisconsin 53706, U.S.A.

Country: United States

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MEDLINETA: Biometrics

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