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A spatial functional count model for heterogeneity analysis in time

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Abstract

A spatial curve dynamical model framework is adopted for functional prediction of counts in a spatiotemporal log-Gaussian Cox process model. Our spatial functional estimation approach handles both wavelet-based heterogeneity analysis in time, and spectral analysis in space. Specifically, model fitting is achieved by minimising the information divergence or relative entropy between the multiscale model underlying the data, and the corresponding candidates in the spatial spectral domain. A simulation study is carried out within the family of log-Gaussian Spatial Autoregressive \(\ell ^{2}\)-valued processes (SAR\(\ell ^{2}\) processes) to illustrate the asymptotic properties of the proposed spatial functional estimators. We apply our modelling strategy to spatiotemporal prediction of respiratory disease mortality.

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Acknowledgements

This work was supported by MCIU/AEI/ERDF, UE grant PGC2018-099549-B-I00 (M.D. Ruiz–Medina, M.P. Frías, A. Torres-Signes), PID2019-107392RB-100 (J. Mateu), and by Grant A-FQM-345-UGR18 (M.D. Ruiz–Medina, M.P. Frías, A. Torres-Signes) cofinanced by ERDF Operational Programme 2014–2020 and the Economy and Knowledge Council of the Regional Government of Andalusia, Spain.

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Authors and Affiliations

  1. Department of Statistics and Operation Research, Faculty of Sciences, University of Málaga, Málaga, Spain

    Antoni Torres-Signes

  2. Department of Statistics and Operation Research, Faculty of Sciences, University of Jaén, Jaén, Spain

    María P. Frías

  3. Department of Mathematics and Operation Research, Faculty of Sciences, University Jaume I, Castellón, Spain

    Jorge Mateu

  4. Department of Statistics and Operation Research, Faculty of Sciences, University of Granada, Granada, Spain

    María D. Ruiz-Medina

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  1. Antoni Torres-Signes
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  2. María P. Frías
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  3. Jorge Mateu
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  4. María D. Ruiz-Medina
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Correspondence to María D. Ruiz-Medina.

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Torres-Signes, A., Frías, M.P., Mateu, J. et al. A spatial functional count model for heterogeneity analysis in time. Stoch Environ Res Risk Assess 35, 1825–1849 (2021). https://doi.org/10.1007/s00477-020-01951-5

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