High resolution simulation of nonstationary Gaussian random fields
Author
Abstract
Suggested Citation
DOI: 10.1016/j.csda.2016.03.005
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Perrin, Olivier & Senoussi, Rachid, 2000. "Reducing non-stationary random fields to stationarity and isotropy using a space deformation," Statistics & Probability Letters, Elsevier, vol. 48(1), pages 23-32, May.
- Peter Guttorp & Stephan R. Sain & Christopher K. Wikle & Grant B. Weller & Daniel S. Cooley & Stephan R. Sain, 2012. "An investigation of the pineapple express phenomenon via bivariate extreme value theory," Environmetrics, John Wiley & Sons, Ltd., vol. 23(5), pages 420-439, August.
- Barndorff-Nielsen, Ole E. & Shephard, Neil, 2006.
"Impact of jumps on returns and realised variances: econometric analysis of time-deformed Levy processes,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 217-252.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Impact of jumps on returns and realised variances: econometric analysis of time-deformed Levy processes," Economics Papers 2003-W12, Economics Group, Nuffield College, University of Oxford.
- Berrocal, Veronica J. & Raftery, Adrian E. & Gneiting, Tilmann & Steed, Richard C., 2010. "Probabilistic Weather Forecasting for Winter Road Maintenance," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 522-537.
- Perrin, Olivier & Senoussi, Rachid, 1999. "Reducing non-stationary stochastic processes to stationarity by a time deformation," Statistics & Probability Letters, Elsevier, vol. 43(4), pages 393-397, July.
- Kleiber, William & Nychka, Douglas, 2012. "Nonstationary modeling for multivariate spatial processes," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 76-91.
- Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Monbet, Valérie & Ailliot, Pierre, 2017. "Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 40-51.
- Gangloff, Hugo & Courbot, Jean-Baptiste & Monfrini, Emmanuel & Collet, Christophe, 2021. "Unsupervised image segmentation with Gaussian Pairwise Markov Fields," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
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.- Monterrubio-Gómez, Karla & Roininen, Lassi & Wade, Sara & Damoulas, Theodoros & Girolami, Mark, 2020. "Posterior inference for sparse hierarchical non-stationary models," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
- Rachid Senoussi & Emilio Porcu, 2022. "Nonstationary space–time covariance functions induced by dynamical systems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 211-235, March.
- Philip A. White & Durban G. Keeler & Daniel Sheanshang & Summer Rupper, 2022. "Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
- Lim, Chae Young & Wu, Wei-Ying, 2022. "Conditions on which cokriging does not do better than kriging," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
- Rajala, T. & Penttinen, A., 2014. "Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 530-541.
- K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
- Laura M. Sangalli, 2021. "Spatial Regression With Partial Differential Equation Regularisation," International Statistical Review, International Statistical Institute, vol. 89(3), pages 505-531, December.
- Michel Harel & Jean-François Lenain & Joseph Ngatchou-Wandji, 2016. "Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 296-321, June.
- 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.
- Finn Lindgren, 2015. "Comments on: Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 35-44, March.
- Almut Veraart & Luitgard Veraart, 2012.
"Stochastic volatility and stochastic leverage,"
Annals of Finance, Springer, vol. 8(2), pages 205-233, May.
- Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
- Andre Python & Andreas Bender & Marta Blangiardo & Janine B. Illian & Ying Lin & Baoli Liu & Tim C.D. Lucas & Siwei Tan & Yingying Wen & Davit Svanidze & Jianwei Yin, 2022. "A downscaling approach to compare COVID‐19 count data from databases aggregated at different spatial scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 202-218, January.
- Vanhatalo, Jarno & Veneranta, Lari & Hudd, Richard, 2012. "Species distribution modeling with Gaussian processes: A case study with the youngest stages of sea spawning whitefish (Coregonus lavaretus L. s.l.) larvae," Ecological Modelling, Elsevier, vol. 228(C), pages 49-58.
- Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
- Yuan Yan & Eva Cantoni & Chris Field & Margaret Treble & Joanna Mills Flemming, 2023. "Spatiotemporal modeling of mature‐at‐length data using a sliding window approach," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
- Jonathan Rougier & Andrew Zammit-Mangion, 2016. "Visualization for Large-scale Gaussian Updates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1153-1161, December.
- Marcelo Cunha & Dani Gamerman & Montserrat Fuentes & Marina Paez, 2017. "A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 919-939, November.
- Caamaño-Carrillo, Christian & Bevilacqua, Moreno & López, Cristian & Morales-Oñate, Víctor, 2024. "Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-hh random fields estimation," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
- Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
- Ephraim M. Hanks, 2017. "Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 497-507, April.
More about this item
Keywords
Circulant embedding; Deformation; Nonstationary; Simulation; Stationary; Warping;All these keywords.
Statistics
Access and download statisticsCorrections
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:eee:csdana:v:101:y:2016:i:c:p:277-288. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.