Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data

Multivariate spatially oriented data sets are prevalent in the environmental and physical sciences. Scientists seek to jointly model multiple variables, each indexed by a spatial location, to capture any underlying spatial association for each variable and associations among the different dependent...

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Vydáno v:	Biometrics Ročník 78; číslo 2; s. 560 - 573
Hlavní autoři:	Zhang, Lu, Banerjee, Sudipto
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Blackwell Publishing Ltd 01.06.2022
Témata:	Algorithms Bayes Theorem Bayesian analysis Bayesian inference Bayesian theory Computer applications Computer Simulation data collection Datasets Dependent variables factor models Linear Models linear models of coregionalization Mathematical models mathematical theory matrix‐normal distribution Multivariate analysis multivariate spatial processes Normal Distribution Physical sciences scalable spatial modeling Spatial data Statistical analysis Statistical inference uncertainty Variables Vegetation index multivariate spatial processes factor models Bayesian inference scalable spatial modeling matrix-normal distribution linear models of coregionalization
ISSN:	0006-341X, 1541-0420, 1541-0420
On-line přístup:	Získat plný text
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