NDLI: Generalized common spatial factor model

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Generalized common spatial factor model

Content Provider	Paperity
Author	Wang, Fujun Wall, Melanie M.
Abstract	There are often two types of correlations in multivariate spatial data: correlations between variables measured at the same locations, and correlations of each variable across the locations. We hypothesize that these two types of correlations are caused by a common spatially correlated underlying factor. Under this hypothesis, we propose a generalized common spatial factor model. The parameters are estimated using the Bayesian method and a Markov chain Monte Carlo computing technique. Our main goals are to determine which observed variables share a common underlying spatial factor and also to predict the common spatial factor. The model is applied to countyâ€�level cancer mortality data in Minnesota to find whether there exists a common spatial factor underlying the cancer mortality throughout the state.
Starting Page	569
Ending Page	582
File Format	HTM / HTML
ISSN	14654644
DOI	10.1093/biostatistics/4.4.569
Issue Number	4
Journal	Biostatistics
Volume Number	4
e-ISSN	14684357
Language	English
Publisher	Oxford University Press
Publisher Date	2003-10-01
Access Restriction	Open
Subject Keyword	Latent Markov chain monte carlo (mcmc) Factor analysis Bayesian Deviance information criterion
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Medicine Statistics, Probability and Uncertainty

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