Computational advances for spatio-temporal multivariate environmental models
نویسندگان
چکیده
Abstract In multivariate Geostatistics, the linear coregionalization model (LCM) has been widely used over last decades, in order to describe spatial dependence which characterizes two or more variables of interest. However, spatio-temporal multiple modeling, identification main elements a space–time (ST-LCM), as well latent structures underlying analyzed phenomenon, represents tough task. this paper, some computational advances support selection an ST-LCM are described, gathering all necessary steps allow analyst easily and properly detect basic components for phenomenon under study. The implemented algorithm is applied on air quality data measured Scotland 2017.
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ژورنال
عنوان ژورنال: Computational Statistics
سال: 2021
ISSN: ['0943-4062', '1613-9658']
DOI: https://doi.org/10.1007/s00180-021-01132-0