A selective view of climatological data and likelihood estimation

Spatial Statistics - Tập 50 - Trang 100596 - 2022
Federico Blasi1, Christian Caamaño-Carrillo2, Moreno Bevilacqua3, Reinhard Furrer4
1Department of Mathematics, University of Zurich, Zurich, Switzerland
2Department of Statistics, Universidad del Bío-Bío, Concepción, Chile
3Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Viña del Mar, Chile
4Department of Mathematics and Department of Computational Science, University of Zurich, Zurich, Switzerland

Tài liệu tham khảo

1970 Alegría, 2021, The F-family of covariance functions: A Matérn analogue for modeling random fields on spheres, Spatial Stat., 43, 10.1016/j.spasta.2021.100512 Allen, 1999, Checking for model consistency in optimal fingerprinting, Clim. Dynam., 15, 419, 10.1007/s003820050291 Appel, 2020, Spatiotemporal multi-resolution approximations for analyzing global environmental data, Spatial Stat., 38, 10.1016/j.spasta.2020.100465 Bachoc, 2020, Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes, Electron. J. Stat., 14, 1962, 10.1214/20-EJS1712 Baddeley, 2017, Local composite likelihood for spatial point processes, Spatial Stat., 22, 261, 10.1016/j.spasta.2017.03.001 Bai, 2014, Efficient pairwise composite likelihood estimation for spatial-clustered data, Biometrics, 7, 661, 10.1111/biom.12199 Banerjee, 2020, Modeling massive spatial datasets using a conjugate Bayesian linear modeling framework, Spatial Stat., 37, 10.1016/j.spasta.2020.100417 Barbian, 2017, Spatial subsemble estimator for large geostatistical data, Spatial Stat., 22, 68, 10.1016/j.spasta.2017.08.004 Bevilacqua, 2021, Non-gaussian geostatistical modeling using (skew) t processes, Scand. J. Stat., 48, 212, 10.1111/sjos.12447 Bevilacqua, 2020, On modelling positive continuous data with spatio-temporal dependence, Environmetrics, 31, 10.1002/env.2632 Bevilacqua, 2020, Families of covariance functions for bivariate random fields on spheres, Spatial Stat., 40, 10.1016/j.spasta.2020.100448 Bevilacqua, 2019, Estimation and prediction using generalized wendland covariance functions under fixed domain asymptotics, Ann. Statist., 47, 828, 10.1214/17-AOS1652 Bevilacqua, 2015, Comparing composite likelihood methods based on pairs for spatial Gaussian random fields, Stat. Comput., 25, 877, 10.1007/s11222-014-9460-6 Bevilacqua, 2012, Estimating space and space–time covariance functions for large data sets: a weighted composite likelihood approach, J. Amer. Statist. Assoc., 107, 268, 10.1080/01621459.2011.646928 Bevilacqua, 2018 Bivand, 2013 Boettiger, 2015, An introduction to Docker for reproducible research, Oper. Syst. Rev., 49, 71, 10.1145/2723872.2723882 Brunner, L., Hauser, M., Lorenz, R., Beyerle, U., 2020. The ETH Zurich CMIP6 Next Generation Archive: Technical Documentation. Technical report, http://dx.doi.org/10.5281/zenodo.3734128. Cameletti, 2019, BayesIan modelling for spatially misaligned health and air pollution data through the inla-spde approach, Spatial Stat., 31, 10.1016/j.spasta.2019.04.001 Cappello, 2021, Time varying complex covariance functions for oceanographic data, Spatial Stat., 42, 10.1016/j.spasta.2020.100426 Castruccio, 2016, Assessing the spatio-temporal structure of annual and seasonal surface temperature for CMIP5 and reanalysis, Spatial Stat., 18, 179, 10.1016/j.spasta.2016.03.004 Cressie, 1993 Cressie, 1994, 4 - models for spatial processes, 93 Cressie, 1996, Asymptotics for REML estimation of spatial covariance parameters, J. Statist. Plann. Inference, 50, 327, 10.1016/0378-3758(95)00061-5 Damian, 2003, Variance modeling for nonstationary spatial processes with temporal replications, J. Geophys. Res.: Atmos., 108 Danabasoglu, 2020, The community earth system model version 2 (CESM2), J. Adv. Modelling Earth Syst., 12 Davis, 2011, Comments on pairwise likelihood in time series models, Statist. Sinica, 21, 255 Eidsvik, 2013, Estimation and prediction in spatial models with block composite likelihoods, J. Comput. Graph. Statist., 23, 295, 10.1080/10618600.2012.760460 Feng, 2014, Composite likelihood estimation for models of spatial ordinal data and spatial proportional data with zero/one values, Environmetrics, 25, 571, 10.1002/env.2306 Flury, 2021, Identification of dominant features in spatial data, Spatial Stat., 41, 10.1016/j.spasta.2020.100483 Fowler, 2007, Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547, 10.1002/joc.1556 Franco-Villoria, 2017, Bootstrap based uncertainty bands for prediction in functional kriging, Spatial Stat., 21, 130, 10.1016/j.spasta.2017.06.005 Fronterrè, 2018, Geostatistical inference in the presence of geomasking: A composite-likelihood approach, Spatial Stat., 28, 319, 10.1016/j.spasta.2018.06.004 Furrer, 2021 Furrer, 2006, Covariance tapering for interpolation of large spatial datasets, J. Comput. Graph. Statist., 15, 502, 10.1198/106186006X132178 Furrer, 2010, Statistical modeling of hot spells and heat waves, Clim. Res., 43, 191, 10.3354/cr00924 Furrer, 2007, Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis, Geophys. Res. Lett., 34 Furrer, 2009, Spatial model fitting for large datasets with applications to climate and microarray problems, Stat. Comput., 19, 113, 10.1007/s11222-008-9075-x Furrer, 2010, Spam: A sparse matrix R package with emphasis on MCMC methods for Gaussian Markov random fields, J. Stat. Softw., 36, 1, 10.18637/jss.v036.i10 Furrer, 2007, Multivariate Bayesian analysis of atmosphere-ocean general circulation models, Environ. Ecol. Stat., 14, 249, 10.1007/s10651-007-0018-z Gerber, 2019, Optimparallel: An R package providing a parallel version of the L-BFGS-B optimization method, R J., 11, 352, 10.32614/RJ-2019-030 Guinness, 2016, Isotropic covariance functions on spheres: Some properties and modeling considerations, J. Multivariate Anal., 143, 143, 10.1016/j.jmva.2015.08.018 Güsewell, 2017, Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason, Global Change Biol., 23, 5189, 10.1111/gcb.13781 Güsewell, 2018, 267 Heagerty, 1998, A composite likelihood approach to binary spatial data, J. Amer. Statist. Assoc., 93, 1099, 10.1080/01621459.1998.10473771 Heaton, 2019, A case study competition among methods for analyzing large spatial data, J. Agric. Biol. Environ. Stat., 24, 398, 10.1007/s13253-018-00348-w Hengl, 2015, Spatial and spatio-temporal modeling of meteorological and climatic variables using open source software, Spatial Stat., 14, 1, 10.1016/j.spasta.2015.06.005 Heyde, 1997 Hong, 2021, Efficiency assessment of approximated spatial predictions for large datasets, Spatial Stat., 43, 10.1016/j.spasta.2021.100517 Houghton, 2001 Hurrell, 2013, 1 Jeong, 2015, A class of Matérn-like covariance functions for smooth processes on a sphere, Spatial Stat., 11, 1, 10.1016/j.spasta.2014.11.001 Joe, 2009, On weighting of bivariate margins in pairwise likelihood, J. Multivariate Anal., 100, 670, 10.1016/j.jmva.2008.07.004 Jones, 2009, Sinh-arcsinh distributions, Biometrika, 96, 761, 10.1093/biomet/asp053 Kalnay, 1996, The NCEP/NCAR 40-year reanalysis project, Am. Meteorol. Soc. Bull., 77, 437, 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 Kaufman, 2008, Covariance tapering for likelihood-based estimation in large spatial data sets, J. Amer. Statist. Assoc., 103, 1545, 10.1198/016214508000000959 Kaufman, 2013, The role of the range parameter for estimation and prediction in geostatistics, Biometrika, 100, 473, 10.1093/biomet/ass079 Kleiber, 2015, Equivalent kriging, Spatial Stat., 12, 31, 10.1016/j.spasta.2015.01.004 Li, 2021, Geospatial constrained optimization to simulate and predict spatiotemporal trends of air pollutants, Spatial Stat., 45, 10.1016/j.spasta.2021.100533 Li, 2018, On approximating optimal weighted composite likelihood method for spatial models, Stat, 7, 10.1002/sta4.194 Lie, 2021, Inference in cylindrical models having latent markovian classes with an application to ocean current data, Spatial Stat., 41, 10.1016/j.spasta.2021.100497 Lindsay, 1988, Composite likelihood methods, Contemp. Math., 80, 221, 10.1090/conm/080/999014 Lindsay, 2011, Issues and strategies in the selection of composite likelihoods, Statist. Sinica, 21, 71 Mardia, 1984, Maximum likelihood estimation of models for residual covariance in spatial regression, Biometrika, 71, 135, 10.1093/biomet/71.1.135 2021 2021 Matérn, 1986 Meehl, G.A., 2019. The Coupled Model Intercomparison Project (CMIP) and interface with IPCC. In: AGU Fall Meeting, WCRP40, San Francisco. Meehl, 1997, Intercomparison makes for a better climate model, Eos, 78 Meehl, 2007, THE WCRP CMIP3 multimodel dataset: A new era in climate change research, Bull. Am. Meteorol. Soc., 88, 1383, 10.1175/BAMS-88-9-1383 NASA, 2021 Nash, 2014 Nychka, 2018, Modeling and emulation of nonstationary Gaussian fields, Spatial Stat., 28, 21, 10.1016/j.spasta.2018.08.006 Pace, 2019, Efficient composite likelihood for a scalar parameter of interest, Stat, 8, 10.1002/sta4.222 Paciorek, 2015, Parallelizing Gaussian process calculations in R, J. Stat. Softw., 63, 1, 10.18637/jss.v063.i10 Pascoe, 2020, Documenting numerical experiments in support of the Coupled Model Intercomparison Project phase 6 CMIP6, Geosci. Model Dev., 13, 2149, 10.5194/gmd-13-2149-2020 Pathakoti, 2021, Assessment of spatio-temporal climatological trends of ozone over the Indian region using machine learning, Spatial Stat., 43, 10.1016/j.spasta.2021.100513 Poggio, 2015, Downscaling and correction of regional climate models outputs with a hybrid geostatistical approach, Spatial Stat., 14, 4, 10.1016/j.spasta.2015.04.006 R. Core Team, 2021 Sain, 2011, A spatial analysis of multivariate output from regional climate models, Ann. Appl. Stat., 5, 150, 10.1214/10-AOAS369 Salvaña, 2020, Nonstationary cross-covariance functions for multivariate spatio-temporal random fields, Spatial Stat., 37, 10.1016/j.spasta.2020.100411 Schmidt, 2020, Flexible spatial covariance functions, Spatial Stat., 37, 10.1016/j.spasta.2020.100416 2007 Stein, 1999 Stein, 2013, Statistical properties of covariance tapers, J. Comput. Graph. Statist., 22, 866, 10.1080/10618600.2012.719844 Stein, 2004, Approximating likelihoods for large spatial data sets, J. Royal Stat. Soc. B, 66, 275, 10.1046/j.1369-7412.2003.05512.x Taylor, K.E., Juckes, M., Balaji, V., Cinquini, L., Denvil, S., Durack, P.J., Elkington, M., Guilyardi, E., Kharin, S., Lautenschlager, M., Lawrence, B., Nadeau, D., Stockhause, M., 2018. CMIP6 Global Attributes, DRS, Filenames, Directory Structure, and CV’s. Technical report, 10 September 2018 (v6.2.7),. Trenberth, 1997, The definition of El Niño, Bull. Am. Meteorol. Soc., 78, 2771, 10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2 Varin, 2011, An overview of composite likelihood methods, Statist. Sinica, 21, 5 Wackernagel, 2006 Waller, 2010, Disease mapping Wendland, 1995, Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree, Adv. Comput. Math., 4, 389, 10.1007/BF02123482 Wendland, 1998, Error estimates for interpolation by compactly supported radial basis functions of minimal degree, J. Approx. Theory, 93, 258, 10.1006/jath.1997.3137 Wilby, 2004 Xu, 2016, Tukey max-stable processes for spatial extremes, Spatial Stat., 18, 431, 10.1016/j.spasta.2016.09.002 Xu, 2017, Tukey g-and-h random fields, J. Amer. Statist. Assoc., 112, 1236, 10.1080/01621459.2016.1205501