A1 Journal article (refereed)
Blind recovery of sources for multivariate space-time random fields (2023)

Muehlmann, C., De Iaco, S., & Nordhausen, K. (2023). Blind recovery of sources for multivariate space-time random fields. Stochastic Environmental Research and Risk Assessment, 37(4), 1593-1613. https://doi.org/10.1007/s00477-022-02348-2

JYU authors or editors

Publication details

All authors or editors: Muehlmann, C.; De Iaco, S.; Nordhausen, K.

Journal or series: Stochastic Environmental Research and Risk Assessment

ISSN: 1436-3240

eISSN: 1436-3259

Publication year: 2023

Publication date: 30/12/2022

Volume: 37

Issue number: 4

Pages range: 1593-1613

Publisher: Springer Science and Business Media LLC

Publication country: Germany

Publication language: English

DOI: https://doi.org/10.1007/s00477-022-02348-2

Research data link: https://www.scottishairquality.scot/data

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/85097

Abstract

With advances in modern worlds technology, huge datasets that show dependencies in space as well as in time occur frequently in practice. As an example, several monitoring stations at different geographical locations track hourly concentration measurements of a number of air pollutants for several years. Such a dataset contains thousands of multivariate observations, thus, proper statistical analysis needs to account for dependencies in space and time between and among the different monitored variables. To simplify the consequent multivariate spatio-temporal statistical analysis it might be of interest to detect linear transformations of the original observations that result in straightforward interpretative, spatio-temporally uncorrelated processes that are also highly likely to have a real physical meaning. Blind source separation (BSS) represents a statistical methodology which has the aim to recover so-called latent processes, that exactly meet the former requirements. BSS was already successfully used in sole temporal and sole spatial applications with great success, but, it was not yet introduced for the spatio-temporal case. In this contribution, a reasonable and innovative generalization of BSS for multivariate space-time random fields (stBSS), under second-order stationarity, is proposed, together with two space-time extensions of the well-known algorithms for multiple unknown signals extraction (stAMUSE) and the second-order blind identification (stSOBI) which solve the formulated problem. Furthermore, symmetry and separability properties of the model are elaborated and connections to the space-time linear model of coregionalization and to the classical principal component analysis are drawn. Finally, the usefulness of the new methods is shown in a thorough simulation study and on a real environmental application.

Keywords: time series; time-series analysis; geographic information; spatial analysis; geostatistics; multivariable methods; signal processing

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1