Composite Likelihood Inference for Multivariate Gaussian Random Fields
In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both the marginal and the cross-spatial dependence of the components of the associated multivariate Gaussia...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8981
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8981
- Palabra clave:
- Cross-covariance
Geostatistics
Large datasets
Computer simulation
Data set
Gaussian method
Geostatistics
Maximum likelihood analysis
Multivariate analysis
Chile
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Composite Likelihood Inference for Multivariate Gaussian Random Fields |
| title |
Composite Likelihood Inference for Multivariate Gaussian Random Fields |
| spellingShingle |
Composite Likelihood Inference for Multivariate Gaussian Random Fields Cross-covariance Geostatistics Large datasets Computer simulation Data set Gaussian method Geostatistics Maximum likelihood analysis Multivariate analysis Chile |
| title_short |
Composite Likelihood Inference for Multivariate Gaussian Random Fields |
| title_full |
Composite Likelihood Inference for Multivariate Gaussian Random Fields |
| title_fullStr |
Composite Likelihood Inference for Multivariate Gaussian Random Fields |
| title_full_unstemmed |
Composite Likelihood Inference for Multivariate Gaussian Random Fields |
| title_sort |
Composite Likelihood Inference for Multivariate Gaussian Random Fields |
| dc.subject.keywords.none.fl_str_mv |
Cross-covariance Geostatistics Large datasets Computer simulation Data set Gaussian method Geostatistics Maximum likelihood analysis Multivariate analysis Chile |
| topic |
Cross-covariance Geostatistics Large datasets Computer simulation Data set Gaussian method Geostatistics Maximum likelihood analysis Multivariate analysis Chile |
| description |
In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both the marginal and the cross-spatial dependence of the components of the associated multivariate Gaussian random field. However, effective estimation methods for these models are somehow unexplored. Maximum likelihood is certainly a useful tool, but it is impractical in all the circumstances where the number of observations is very large. In this work, we consider two possible approaches based on composite likelihood for multivariate covariance model estimation. We illustrate, through simulation experiments, that our methods offer a good balance between statistical efficiency and computational complexity. Asymptotic properties of the proposed estimators are assessed under increasing domain asymptotics. Finally, we apply the method for the analysis of a bivariate dataset on chlorophyll concentration and sea surface temperature in the Chilean coast. © 2016, International Biometric Society. |
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2016 |
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2016 |
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2020-03-26T16:32:42Z |
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2020-03-26T16:32:42Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Artículo |
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publishedVersion |
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Journal of Agricultural, Biological, and Environmental Statistics; Vol. 21, Núm. 3; pp. 448-469 |
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10857117 |
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https://hdl.handle.net/20.500.12585/8981 |
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10.1007/s13253-016-0256-3 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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7102698888 57188537306 54783771000 21934725400 |
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Journal of Agricultural, Biological, and Environmental Statistics; Vol. 21, Núm. 3; pp. 448-469 10857117 10.1007/s13253-016-0256-3 Universidad Tecnológica de Bolívar Repositorio UTB 7102698888 57188537306 54783771000 21934725400 |
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https://hdl.handle.net/20.500.12585/8981 |
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eng |
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eng |
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Atribución-NoComercial 4.0 Internacional |
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Recurso electrónico |
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application/pdf |
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Springer New York LLC |
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Springer New York LLC |
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2020-03-26T16:32:42Z2020-03-26T16:32:42Z2016Journal of Agricultural, Biological, and Environmental Statistics; Vol. 21, Núm. 3; pp. 448-46910857117https://hdl.handle.net/20.500.12585/898110.1007/s13253-016-0256-3Universidad Tecnológica de BolívarRepositorio UTB7102698888571885373065478377100021934725400In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both the marginal and the cross-spatial dependence of the components of the associated multivariate Gaussian random field. However, effective estimation methods for these models are somehow unexplored. Maximum likelihood is certainly a useful tool, but it is impractical in all the circumstances where the number of observations is very large. In this work, we consider two possible approaches based on composite likelihood for multivariate covariance model estimation. We illustrate, through simulation experiments, that our methods offer a good balance between statistical efficiency and computational complexity. Asymptotic properties of the proposed estimators are assessed under increasing domain asymptotics. Finally, we apply the method for the analysis of a bivariate dataset on chlorophyll concentration and sea surface temperature in the Chilean coast. © 2016, International Biometric Society.Recurso electrónicoapplication/pdfengSpringer New York LLChttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84978042302&doi=10.1007%2fs13253-016-0256-3&partnerID=40&md5=2b4c4cbf8bdda67553df93ec78feb0e8Composite Likelihood Inference for Multivariate Gaussian Random Fieldsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Cross-covarianceGeostatisticsLarge datasetsComputer simulationData setGaussian methodGeostatisticsMaximum likelihood analysisMultivariate analysisChileBevilacqua M.Alegria A.Velandia D.Porcu E.Apanasovich, T., Genton, M., Sun, Y., A valid Matérn class of cross-covariance functions for multivariate random fields with any number of components (2012) Journal of the American Statistical Association, 97, pp. 15-30Bevilacqua, M., Fassò, A., Gaetan, C., Porcu, E., Velandia, D., Covariance tapering for multivariate Gaussian random fields estimation (2016) Statistical Methods & Applications, 25 (1), pp. 21-37Bevilacqua, M., Gaetan, C., Comparing composite likelihood methods based on pairs for spatial Gaussian random fields (2015) Statistics and Computing, 25, pp. 877-892Bevilacqua, M., Gaetan, C., Mateu, J., Porcu, E., Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach (2012) Journal of the American Statistical Association, 107, pp. 268-280Bevilacqua, M., Vallejos, R., Velandia, D., Assessing the significance of the correlation between the components of a bivariate Gaussian random field (2015) Environmetrics, 26, pp. 545-556Boyce, D.G., Lewis, M.R., Worm, B., Global phytoplankton decline over the past century, (2010) Nature. International weekly journal of science, p. 466Castruccio, S., Huser, R., Genton, M.G., High-order composite likelihood inference for max-stable distributions and processes, (2016) Journal of Computational and Graphical StatisticsDaley, D., Porcu, E., Bevilacqua, M., Classes of compactly supported covariance functions for multi- variate random fields (2015) Stoch Environ Res Risk Assess, 29, pp. 1249-1263Davis, R., Yau, C.-Y., Comments on pairwise likelihood in time series models (2011) Statistica Sinica, 21, pp. 255-277Doney, S.C., Ruckelshaus, M., Duffy, J.E., Barry, J.P., Chan, F., English, C.A., Galindo, H.M., Talley, L.D., Annual Review of Marine Science (2012) Nature. International weekly journal of science, 4, pp. 11-37Eidsvik, J., Shaby, B., Reich, B., Wheeler, M., Niemi, J., Estimation and prediction in spatial models with block composite likelihoods (2014) Journal of Computational and Graphical Statistics, 29, pp. 295-315Furrer, R., Bachoc, F., and Du, J. (2016), “Asymptotic properties of multivariate tapering for estimation and prediction,” Journal of Multivariate Analysis, In pressFurrer, R., Genton, M.G., Nychka, D., Covariance tapering for interpolation of large spatial datasets (2006) Journal of Computational and Graphical Statistics, 15, pp. 502-523Genton, M.G., Padoan, S., Sang, H., Multivariate max-stable spatial processes (2015) Biometrika, 102, pp. 215-230Genton, M., and Kleiber, W. (2015), “Cross-Covariance Functions for Multivariate Geostatistics,” Statistical Science, in pressGneiting, T., Compactly supported correlation functions (2002) Journal of Multivariate Analysis, 83, pp. 493-508Gneiting, T., Genton, M.G., Guttorp, P., Geostatistical space–time models, stationarity, separability and full symmetry (2007) Statistical Methods for Spatio-Temporal Systems, pp. 151-175. , Finkenstadt B, Held L, Isham V, (eds), Chapman & Hall/CRC, Boca Raton, FLGneiting, T., Kleiber, W., Schlather, M., Matérn Cross-Covariance Functions for Multivariate Random Fields (2010) Journal of the American Statistical Association, 105, pp. 1167-1177Goulard, M., Voltz, M., Linear coregionalization model: Tools for estimation and choice of cross-variogram matrix (1992) Mathematical Geology, 24, pp. 269-286Heagerty, P., Lumley, T., Window subsampling of estimating functions with application to regression models (2000) Journal of the American Statistical Association, 95, pp. 197-211Joe, H., Lee, Y., On weighting of bivariate margins in pairwise likelihood (2009) Journal of Multivariate Analysis, 100, pp. 670-685Kaufman, C.G., Schervish, M.J., Nychka, D.W., Covariance tapering for likelihood-based estimation in large spatial data sets (2008) Journal of the American Statistical Association, 103, pp. 1545-1555Lee, A., Yau, C., Giles, M., Doucet, A., Holmes, C., On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods (2010) Journal of Computational and Graphical Statistics, 19, pp. 769-789Lee, Y., Lahiri, S., Least squares variogram fitting by spatial subsampling (2002) Journal of the Royal Statistical Society B, 64, pp. 837-854Li, B., Zhang, H., An approach to modeling asymmetric multivariate spatial covariance structures (2011) Journal of Multivariate Analysis, 102, pp. 1445-1453Lindsay, B., Composite likelihood methods (1988) Contemporary Mathematics, 80, pp. 221-239Padoan, S.A., Bevilacqua, M., Analysis of Random Fields Using CompRandFld (2015) Journal of Statistical Software, 63, pp. 1-27Pelletier, B., Dutilleul, P., Larocque, G., Fyles, J., Fitting the linear model of coregionalization by generalized least squares (2004) Mathematical Geology, 36 (3), pp. 323-343Porcu, E., Daley, D., Buhmann, M., Bevilacqua, M., Radial basis functions with compact support for multivariate geostatistics (2013) Stochastic Environmental Research and Risk Assessment, 27, pp. 909-922Shaby, B., Ruppert, D., Tapered covariance: Bayesian estimation and asymptotics (2012) Journal of Computational and Graphical Statistics, 21, pp. 433-452Stein, M., Space–time covariance functions (2005) Journal of the American Statistical Association, 100, pp. 310-321Stein, M., Chi, Z., Welty, L., Approximating likelihoods for large spatial data sets (2004) Journal of the Royal Statistical Society B, 66, pp. 275-296Suchard, M., Wang, Q., anf, J., Frelinger, C.C., Cron, A., West, M., Understanding GPU programming for statistical computation: studies in massively parallel massive mixtures (2010) Journal of Computational and Graphical Statistics, 19, pp. 419-438Varin, C., Reid, N., Firth, D., An overview of composite likelihood methods (2011) Statistica Sinica, 21, pp. 5-42Varin, C., Vidoni, P., A note on composite likelihood inference and model selection (2005) Biometrika, 52, pp. 519-528Wackernagel, H., (2003) Multivariate Geostatistics: An Introduction with Applications, , Springer, New YorkWood, S., (2006) Generalized Additive Models: An Introduction with R, , Chapman and Hall CRC, Boca RatonZhang, H., Maximum-likelihood estimation for multivariate spatial linear coregionalization models (2007) Environmetrics, 18, pp. 125-139http://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8981/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8981oai:repositorio.utb.edu.co:20.500.12585/89812021-02-02 15:17:43.828Repositorio Institucional UTBrepositorioutb@utb.edu.co |