A scalable approach to fit spatio-temporal ecological models in large data sets using the bigDM package
A. Adin Urtasun, E. Orozco-Acosta, M. D. Ugarte
Research in spatio-temporal disease mapping has been very fruitful in recent decades and numerous statistical models have been propose to study the geographic distribution of a disease and its evolution in time. However, the scalability of these models has not been studied in depth yet. In this work, we propose a “divide-and-conquer” based methodology as an alternative modelling approach to the commonly used disease mapping models in order to analyze high-dimensional spatio-temporal data. The methods and algorithms proposed in this work are implemented in the open-source R package "bigDM" (https://github.com/spatialstatisticsupna/bigDM), which allows the user to adapt the modelling scheme to their own processing architecture by performing both parallel and/or distributed computation strategies. Currently, we are working on the development of scalable ecological regression models taking into account the spatial and/or spatio-temporal confounding issues between fixed and random effects.
Keywords: Disease mapping, Hierarchical models, Laplace approximations, Massive data, Scalable modelling
Scheduled
GT19 Time Space Statistics
June 7, 2022 12:00 PM
Conference hall
Other papers in the same session
M. P. Frías Bustamante, A. Torres Signes, M. D. Ruiz Medina, J. Mateu Mahiques
M. P. Bohorquez Castañeda, A. M. Forero Sanabria, J. Mateu Mahiques, R. R. Renteria Ramos
A. E. Madrid, J. L. Romero, J. M. Angulo
M. I. Borrajo García, W. González Manteiga, M. D. Martínez Miranda
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6/8/22
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5/31/22
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5/27/22
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5/23/22
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5/16/22
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