An extended PDE‐based statistical spatio‐temporal model that suppresses the Gibbs phenomenon
Author
Abstract
Suggested Citation
DOI: 10.1002/env.2831
Download full text from publisher
References listed on IDEAS
- Xiao Liu & Kyongmin Yeo & Siyuan Lu, 2022. "Statistical Modeling for Spatio-Temporal Data From Stochastic Convection-Diffusion Processes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1482-1499, September.
- Fabio Sigrist & Hans R. Künsch & Werner A. Stahel, 2015. "Stochastic partial differential equation based modelling of large space–time data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 3-33, January.
- Cooley, Daniel & Nychka, Douglas & Naveau, Philippe, 2007. "Bayesian Spatial Modeling of Extreme Precipitation Return Levels," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 824-840, September.
- Stroud, Jonathan R. & Stein, Michael L. & Lesht, Barry M. & Schwab, David J. & Beletsky, Dmitry, 2010. "An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 978-990.
- Kang, Jian & Johnson, Timothy D. & Nichols, Thomas E. & Wager, Tor D., 2011. "Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 124-134.
- Brian J. Reich & Joseph Guinness & Simon N. Vandekar & Russell T. Shinohara & Ana†Maria Staicu, 2018. "Fully Bayesian spectral methods for imaging data," Biometrics, The International Biometric Society, vol. 74(2), pages 645-652, June.
- 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.
- Matthias Katzfuss & Noel Cressie, 2011. "Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets," Journal of Time Series Analysis, Wiley Blackwell, vol. 32, pages 430-446, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Guanzhou Wei & Venkat Krishnan & Yu Xie & Manajit Sengupta & Yingchen Zhang & Haitao Liao & Xiao Liu, 2024. "A Statistical Model for Multisource Remote-Sensing Data Streams of Wildfire Aerosol Optical Depth," INFORMS Joural on Data Science, INFORMS, vol. 3(2), pages 162-178, October.
- Marcin Jurek & Matthias Katzfuss, 2023. "Scalable spatio‐temporal smoothing via hierarchical sparse Cholesky decomposition," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
- S. R. Johnson & S. E. Heaps & K. J. Wilson & D. J. Wilkinson, 2023. "A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
- Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
- Park, Eunchun & Brorsen, B. Wade & Harri, Ardian, 2016. "Using Bayesian Spatial Smoothing and Extreme Value Theory to Develop Area-Yield Crop Insurance Rating," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235754, Agricultural and Applied Economics Association.
- Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.
- Fatima Palacios‐Rodriguez & Elena Di Bernardino & Melina Mailhot, 2023. "Smooth copula‐based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada," Environmetrics, John Wiley & Sons, Ltd., vol. 34(3), May.
- Daniela Castro-Camilo & Raphaël Huser & Håvard Rue, 2019. "A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 517-534, September.
- Isabelle Grenier & Bruno Sansó & Jessica L. Matthews, 2024. "Multivariate nearest‐neighbors Gaussian processes with random covariance matrices," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), May.
- Salaheddine El Adlouni, 2018. "Quantile regression C-vine copula model for spatial extremes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(1), pages 299-317, October.
- Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016.
"Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem,"
Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 143-161, March.
- Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016. "Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 143-161, March.
- 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.
- Joaquim Henriques Vianna Neto & Alexandra M. Schmidt & Peter Guttorp, 2014. "Accounting for spatially varying directional effects in spatial covariance structures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 103-122, January.
- Stephenson Alec G. & Tawn Jonathan A., 2013. "Determining the Best Track Performances of All Time Using a Conceptual Population Model for Athletics Records," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 67-76, March.
- 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.
- Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
- Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
- Gromenko, Oleksandr & Kokoszka, Piotr, 2013. "Nonparametric inference in small data sets of spatially indexed curves with application to ionospheric trend determination," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 82-94.
- Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
- Miranda J. Fix & Daniel S. Cooley & Emeric Thibaud, 2021. "Simultaneous autoregressive models for spatial extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:envmet:v:35:y:2024:i:2:n:e2831. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.