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A partial envelope approach for modelling multivariate spatial-temporal data
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DOI：10.1002/cjs.70052.png)
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Abstract
En 中文
In the new era of big data, modelling multivariate spatial-temporal data is a challenging task due to both the high dimensionality of the features and complex associations among the responses across different locations and time points. To improve the estimation efficiency, we propose a spatial-temporal partial envelope model that is parsimonious and effective in modelling high-dimensional spatial-temporal data. The partial envelope model is proposed under a linear coregionalization model framework, which allows heterogeneous covariance structures for different variables of the response vector. We study the asymptotic behaviour of the estimator and conduct a thorough simulation study to demonstrate the soundness and effectiveness of the proposed method. We also apply the proposed model to analyze the crowdsourcing weather data collected from personal weather stations in the city of Syracuse, New York, USA.Dans la nouvelle & egrave;re des m & eacute;gadonn & eacute;es, la mod & eacute;lisation de donn & eacute;es spatio-temporelles multivari & eacute;es constitue une t & acirc;che difficile en raison & agrave; la fois de la haute dimensionnalit & eacute; des variables explicatives et des associations complexes entre les r & eacute;ponses, observ & eacute;es & agrave; diff & eacute;rents emplacements et moments. Pour am & eacute;liorer l'efficacit & eacute; de l'estimation, nous proposons un mod & egrave;le d'enveloppe partielle spatio-temporelle, parcimonieux et efficace pour mod & eacute;liser des donn & eacute;es spatio-temporelles de haute dimension. Ce mod & egrave;le est d & eacute;velopp & eacute; dans le cadre d'un mod & egrave;le de co-r & eacute;gionalisation lin & eacute;aire, qui autorise des structures de covariance h & eacute;t & eacute;rog & egrave;nes pour les diff & eacute;rentes variables du vecteur de r & eacute;ponse. Nous & eacute;tudions le comportement asymptotique de l'estimateur et r & eacute;alisons une & eacute;tude de simulation approfondie afin de d & eacute;montrer la validit & eacute; et l'efficacit & eacute; de la m & eacute;thode propos & eacute;e. Nous appliquons & eacute;galement ce mod & egrave;le & agrave; l'analyse de donn & eacute;es m & eacute;t & eacute;orologiques issues de l'externalisation ouverte, collect & eacute;es par des stations m & eacute;t & eacute;orologiques personnelles dans la ville de Syracuse, dans l'& Eacute;tat de New York, aux & Eacute;tats-Unis.
Keywords:
Estimation efficiency
linear coregionalization model
reducing subspace
space-time dependence
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