Analysis of massive marked point patterns with stochastic partial differential equations

Spatial Statistics - Tập 14 - Trang 179-196 - 2015
Virgilio Gómez-Rubio1, Michela Cameletti2, Francesco Finazzi2
1Department of Mathematics, School of Industrial Engineering, University of Castilla-La Mancha, 02071 Albacete, Spain
2Department of Management, Economics and Quantitative Methods, University of Bergamo, 24127, Bergamo, Italy

Tài liệu tham khảo

Akers, 2014, Multinomial logistic regression model for predicting tornado intensity based on path length and width, World Environ., 4, 61 Baddeley, 2005, spatstat: An R package for analyzing spatial point patterns, J. Stat. Softw., 12, 1, 10.18637/jss.v012.i06 Bivand, 2014, Approximate Bayesian inference for spatial econometrics models, Spat. Stat., 9, 146, 10.1016/j.spasta.2014.01.002 Bivand, R.S., Lewin-Koh, N., 2014. maptools: Tools for reading and handling spatial objects. R package version 0.8-30. Bivand, 2013 Blangiardo, 2015 Blangiardo, 2013, Spatial and spatio-temporal models with R-INLA, Spat. Spat.-Temporal Epidemiol., 4, 33, 10.1016/j.sste.2012.12.001 Cameletti, 2013, Spatio-temporal modeling of particulate matter concentration through the SPDE approach, AStA Adv. Stat. Anal., 97, 109, 10.1007/s10182-012-0196-3 Cosandey-Godin, 2015, Applying Bayesian spatio-temporal models to fisheries bycatch in the Canadian Arctic, Can. J. Fish. Aquat. Sci., 72, 186, 10.1139/cjfas-2014-0159 Diggle, 2007, Second-order analysis of inhomogeneous spatial point processes using case-control data, Biometrics, 63, 550, 10.1111/j.1541-0420.2006.00683.x Diggle, 2013, Spatial and spatio-temporal log-Gaussian Cox processes: Extending the geostatistical paradigm, Statist. Sci., 28, 542, 10.1214/13-STS441 Diggle, 2005, Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in cornwall, UK, J. Roy. Statist. Soc. Ser. C, 54, 645, 10.1111/j.1467-9876.2005.05373.x Elsner, 2014, Tornado intensity estimated from damage path dimensions, PLoS One, 9, 10.1371/journal.pone.0107571 Elsner, 2013, A spatial point process model for violent tornado occurrence in the US Great Plains, Math. Geosci., 45, 667, 10.1007/s11004-013-9458-1 Fuglstad, G.-A., Simpson, D., Lindgren, F., Rue, H., 2014. Does non-stationary spatial data always require non-stationary random fields? ArXiv e-prints. URL: http://adsabs.harvard.edu/abs/2014arXiv1409.0743F. 2010 Gómez-Rubio, 2014, Spatial models using Laplace approximation methods, 1401 Grilli, 2014, Bayesian estimation with integrated nested Laplace approximation for binary logit mixed models, J. Stat. Comput. Simul. Illian, 2013, Fitting complex ecological point process models with integrated nested Laplace approximation, Methods Ecol. Evol., 4, 305, 10.1111/2041-210x.12017 Illian, 2012, A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA), J. R. Stat. Soc. Ser. B, 6, 1499 Illian, 2012, Using INLA to fit a complex point process model with temporally varying effects—A case study, J. Environ. Stat., 3, 1 Ingebrigtsen, 2014, Spatial models with explanatory variables in the dependence structure, Spat. Stat., 8, 20, 10.1016/j.spasta.2013.06.002 Jona Lasinio, 2012, Discussing the “big n problem”, Stat. Methods Appl., 1 Karpman, 2013, A point process model for tornado report climatology, Stat, 2, 1, 10.1002/sta4.14 Knorr-Held, 2001, A shared component model for detecting joint and selective clustering of two diseases, J. Roy. Statist. Soc. Ser. A, 164, 73, 10.1111/1467-985X.00187 Krainski, E.T., Lindgren, F., 2014. The R-INLA tutorial: SPDE models. R-INLA. URL: http://www.math.ntnu.no/inla/r-inla.org/tutorials/spde/spde-tutorial.pdf. Liang, 2009, Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information, Ann. Appl. Stat., 3, 943, 10.1214/09-AOAS240 Lindgren, 2015, Bayesian spatial modelling with R-INLA, J. Stat. Softw., 63, 1, 10.18637/jss.v063.i19 Lindgren, 2011, An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach (with discussion), J. R. Stat. Soc. Ser. B, 73, 423, 10.1111/j.1467-9868.2011.00777.x Martins, 2013, Bayesian computing with INLA: New features, Comput. Statist. Data Anal., 67, 68, 10.1016/j.csda.2013.04.014 Møller, 1998, Log Gaussian Cox processes, Scand. J. Statist., 25, 451, 10.1111/1467-9469.00115 Møller, 2007, Modern statistics for spatial point processes, Scand. J. Statist., 34, 643 Muff, 2015, Bayesian analysis of measurement error models using integrated nested Laplace approximations, J. Roy. Statist. Soc. Ser. C, 64, 231, 10.1111/rssc.12069 Musenge, 2013, Bayesian analysis of zero inflated spatiotemporal HIV/TB child mortality data through the INLA and SPDE approaches: Applied to data observed between 1992 and 2010 in rural North East South Africa, Int. J. Appl. Earth Obs. Geoinf., 22, 86, 10.1016/j.jag.2012.04.001 Papoila, 2014, Stomach cancer incidence in Southern Portugal 1998–2006: A spatio-temporal analysis, Biom. J., 56, 403, 10.1002/bimj.201200264 Pereira, 2013, Quantification of annual wildfire risk; a spatio-temporal point process approach, Statistica, 73, 55 2014 Rue, 2005 Rue, 2009, Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, J. R. Stat. Soc. Ser. B, 2, 1 Rue, H., Martino, S., Lindgren, F., Simpson, D., Riebler, A., Krainski, E.T., 2014. INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximation. R package version 0.0-1406288160. Saez, 2012, Space–time interpolation of daily air temperatures, J. Environ. Stat., 3, 1 Scott, 1992 Simpson, D., Illian, J., Lindgren, F., Sørbye, S., Rue, H., 2013. Going off grid: Computationally efficient inference for log-Gaussian Cox processes. ArXiv e-prints. URL: http://adsabs.harvard.edu/abs/2011arXiv1111.0641S. Simpson, 2012, In order to make spatial statistics computationally feasible, we need to forget about the covariance function, Environmetrics, 23, 65, 10.1002/env.1137 Simpson, 2012, Think continuous: Markovian Gaussian models in spatial statistics, Spat. Stat., 1, 16, 10.1016/j.spasta.2012.02.003 Wikle, 2003, Climatological analysis of tornado report counts using a hierarchical Bayesian spatiotemporal model, J. Geophys. Res.: Atmos., 108, 10.1029/2002JD002806