2011
DOI: 10.1007/s10260-011-0177-9
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Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland

Abstract: A hierarchical Bayesian factor model for multivariate spatially correlated data is proposed. Multiple cancer incidence data in Scotland are jointly analyzed, looking for common components, able to detect etiological factors of diseases hidden behind the data. The proposed method searches factor scores incorporating a dependence within observations due to a geographical structure. The great flexibility of the Bayesian approach allows the inclusion of prior opinions about adjacent regions having highly correlate… Show more

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Cited by 14 publications
(11 citation statements)
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“…Previous work has developed Kronecker‐structured covariance matrices of 2 or 3 components. Repeated measures are the most common application of these methods, as well as applications to spatial and imaging data. Additional structure may be imposed on the subblocks, such as circulant or Toeplitz .…”
Section: Introductionmentioning
confidence: 99%
“…Previous work has developed Kronecker‐structured covariance matrices of 2 or 3 components. Repeated measures are the most common application of these methods, as well as applications to spatial and imaging data. Additional structure may be imposed on the subblocks, such as circulant or Toeplitz .…”
Section: Introductionmentioning
confidence: 99%
“…Can Bayesian methods be used for factor analysis? Yes: (e.g., Arminger & Muthén, 1998;Ghosh & Dunson, 2009;Merkle & Rosseel, 2016;Mezzetti, 2012;Song & Lee, 2001. Can Bayesian methods be applied to item response theory?…”
Section: Bayesian Analysis Applies To Any Parameterized Model Of Datamentioning
confidence: 99%
“…A spatio-temporal mixture model was proposed to analyse the space-time variation in respiratory cancers in the state of South Carolina [24]. [25] proposed a hierarchical Bayesian factor model for spatially correlated data to explain across and within county correlations of cancer incidence rates by assuming that all different cancer types (12 for females and 10 for males) share one or more spatially correlated common factors. The model was to age-standardised cancer incidence rates by sex in 56 counties of Scotland.…”
Section: Introductionmentioning
confidence: 99%