Generalized spatial dynamic factor models

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dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorGamerman, Dani
dc.contributor.authorSalazar, Esther
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorGamerman, Dani
dc.creatorSalazar, Esther
dc.date.accessioned2022-10-04T20:06:22Z
dc.date.available2022-10-04T20:06:22Z
dc.date.issued2011
dc.description.otherThis paper introduces a new class of spatio-temporal models for measurements belonging to the exponential family of distributions. In this new class, the spatial and temporal components are conditionally independently modeled via a latent factor analysis structure for the (canonical) transformation of the measurements mean function. The factor loadings matrix is responsible for modeling spatial variation, while the common factors are responsible for modeling the temporal variation. One of the main advantages of our model with spatially structured loadings is the possibility of detecting similar regions associated to distinct dynamic factors. We also show that the new class outperforms a large class of spatial-temporal models that are commonly used in the literature. Posterior inference for fixed parameters and dynamic latent factors is performed via a custom tailored Markov chain Monte Carlo scheme for multivariate dynamic systems that combines extended Kalman filter-based Metropolis–Hastings proposal densities with block-sampling schemes. Factor model uncertainty is also fully addressed by a reversible jump Markov chain Monte Carlo algorithm designed to learn about the number of common factors. Three applications, two based on synthetic Gamma and Bernoulli data and one based on real Bernoulli data, are presented in order to illustrate the flexibility and generality of the new class of models, as well as to discuss features of the proposed MCMC algorithm.pt_BR
dc.format.extentp. 1319-1330pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doihttps://doi.org/10.1016/j.csda.2010.09.020pt_BR
dc.identifier.issue3pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4128
dc.identifier.volume55pt_BR
dc.language.isoInglêspt_BR
dc.publisherElsevierpt_BR
dc.relation.ispartofComputational Statistics & Data Analysispt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORpt_BR
dc.subject.keywordsExponential familypt_BR
dc.subject.keywordsFactor modelpt_BR
dc.subject.keywordsGaussian processpt_BR
dc.subject.keywordsMarkov chain Monte Carlopt_BR
dc.subject.keywordsReversible jumppt_BR
dc.subject.keywordsSampling schemespt_BR
dc.titleGeneralized spatial dynamic factor modelspt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.sciencedirect.com/science/article/pii/S0167947310003634?via%3Dihub
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca

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