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Spatio-temporal modeling of particulate matter concentration through the SPDE approach

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Cited by:

  1. Damaris K. Kinyoki & Samuel O. Manda & Grainne M. Moloney & Elijah O. Odundo & James A. Berkley & Abdisalan M. Noor & Ngianga-Bakwin Kandala, 2017. "Modelling the Ecological Comorbidity of Acute Respiratory Infection, Diarrhoea and Stunting among Children Under the Age of 5 Years in Somalia," International Statistical Review, International Statistical Institute, vol. 85(1), pages 164-176, April.
  2. Márcio Poletti Laurini, 2017. "A spatial error model with continuous random effects and an application to growth convergence," Journal of Geographical Systems, Springer, vol. 19(4), pages 371-398, October.
  3. Leonardo Padilla & Bernado Lagos‐Álvarez & Jorge Mateu & Emilio Porcu, 2020. "Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
  4. Ali M. Mosammam & Jorge Mateu, 2018. "A penalized likelihood method for nonseparable space–time generalized additive models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 333-357, July.
  5. Somnath Chaudhuri & Gerard Giménez-Adsuar & Marc Saez & Maria A. Barceló, 2022. "PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain," IJERPH, MDPI, vol. 19(8), pages 1-22, April.
  6. Carson, Stuart & Mills Flemming, Joanna, 2014. "Seal encounters at sea: A contemporary spatial approach using R-INLA," Ecological Modelling, Elsevier, vol. 291(C), pages 175-181.
  7. Yuheng Ling, 2020. "Time, space and hedonic prediction accuracy: evidence from Corsican apartment markets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 367-388, April.
  8. Nathan A. Ryder & Joshua P. Keller, 2023. "Spatiotemporal Exposure Prediction with Penalized Regression," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 260-278, June.
  9. Marvin, Dario & Nespoli, Lorenzo & Strepparava, Davide & Medici, Vasco, 2022. "A data-driven approach to forecasting ground-level ozone concentration," International Journal of Forecasting, Elsevier, vol. 38(3), pages 970-987.
  10. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.
  11. Nicoletta D’Angelo & Antonino Abbruzzo & Giada Adelfio, 2021. "Spatio-Temporal Spread Pattern of COVID-19 in Italy," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
  12. Guido Fioravanti & Michela Cameletti & Sara Martino & Giorgio Cattani & Enrico Pisoni, 2022. "A spatiotemporal analysis of NO2 concentrations during the Italian 2020 COVID‐19 lockdown," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
  13. Jonas Wallin & David Bolin, 2015. "Geostatistical Modelling Using Non-Gaussian Matérn Fields," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 872-890, September.
  14. Jussi Jousimo & Otso Ovaskainen, 2016. "A Spatio-Temporally Explicit Random Encounter Model for Large-Scale Population Surveys," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-19, September.
  15. Braulio-Gonzalo, Marta & Bovea, María D. & Jorge-Ortiz, Andrea & Juan, Pablo, 2021. "Which is the best-fit response variable for modelling the energy consumption of households? An analysis based on survey data," Energy, Elsevier, vol. 231(C).
  16. Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
  17. I Gede Nyoman Mindra Jaya & Henk Folmer, 2024. "High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia," Mathematics, MDPI, vol. 12(18), pages 1-29, September.
  18. Laurini, Márcio Poletti, 2017. "The spatio-temporal dynamics of ethanol/gasoline price ratio in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1-12.
  19. Jonathan Rougier & Aoibheann Brady & Jonathan Bamber & Stephen Chuter & Sam Royston & Bramha Dutt Vishwakarma & Richard Westaway & Yann Ziegler, 2023. "The scope of the Kalman filter for spatio‐temporal applications in environmental science," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
  20. Bivand, Roger & Gómez-Rubio, Virgilio & Rue, Håvard, 2015. "Spatial Data Analysis with R-INLA with Some Extensions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i20).
  21. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
  22. Jacqueline D. Seufert & Andre Python & Christoph Weisser & Elías Cisneros & Krisztina Kis‐Katos & Thomas Kneib, 2022. "Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2121-2155, October.
  23. Olafsdottir, Helga Kristin & Rootzén, Holger & Bolin, David, 2024. "Locally tail-scale invariant scoring rules for evaluation of extreme value forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1701-1720.
  24. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
  25. Márcio Poletti Laurini, 2017. "A continuous spatio-temporal model for house prices in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 235-269, January.
  26. Zammit-Mangion, Andrew & Rougier, Jonathan, 2018. "A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 116-130.
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