Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

 
 
Routledge Cavendish (Verlag)
  • erschienen am 7. Dezember 2018
  • |
  • 298 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-0-429-62821-4 (ISBN)
 
Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matern covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications.





This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications:





* Spatial and spatio-temporal models for continuous outcomes


* Analysis of spatial and spatio-temporal point patterns


* Coregionalization spatial and spatio-temporal models


* Measurement error spatial models


* Modeling preferential sampling


* Spatial and spatio-temporal models with physical barriers


* Survival analysis with spatial effects


* Dynamic space-time regression


* Spatial and spatio-temporal models for extremes


* Hurdle models with spatial effects


* Penalized Complexity priors for spatial models





All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book.





The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.
 

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. <b>Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA</b> describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matern covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications.



This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications:



* Spatial and spatio-temporal models for continuous outcomes


* Analysis of spatial and spatio-temporal point patterns


* Coregionalization spatial and spatio-temporal models


* Measurement error spatial models


* Modeling preferential sampling


* Spatial and spatio-temporal models with physical barriers


* Survival analysis with spatial effects


* Dynamic space-time regression


* Spatial and spatio-temporal models for extremes


* Hurdle models with spatial effects


* Penalized Complexity priors for spatial models



All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book.



The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
978-0-429-62821-4 (9780429628214)
Elias T. Krainski is a Professor Adjunto in the Department of Statistics, Universidade Federal do Parana (Curitiba, Brazil). He has been working on new space-time models and applications in epidemiology and fisheries with INLA and SPDE.





Virgilio Gomez-Rubio is an Associate Professor in the Department of Mathematics, Universidad de Castilla-La Mancha (Albacete, Spain). His research interests are on Bayesian inference, spatial statistics and computational statistics. He has also developed several packages for the R language on spatial data analysis and Bayesian computation.





Haakon Bakka is a Post Doctoral Fellow at the King Abdullah University of Science and Technology. He has given many courses in both INLA and the SPDE approach, and parts of his research on spatial models are included in this book.





Amanda Lenzi is a Post-Doctoral Fellow at the King Abdullah University of Science and Technology in Saudi Arabia, where she is part of the Spatio-Temporal Statistics and Data Science Group. Her research interest is on spatial and spatio-temporal statistics with applications in environmental science, especially in wind energy.





Daniela Castro-Camilo is a Post-Doctoral Fellow working in the Extreme Statistics Research Group at the King Abdullah University of Science and Technology, in Saudi Arabia. Her research interest is on the theory and applications of multivariate and spatial extremes, with a particular focus in environmental applications.





Daniel Simpson is an Assistant Professor in the Department of Statistical Sciences, University of Toronto. His research interests are on Computational Statistics, Spatial Statistics, Bayesian Statistics, and Numerical Linear Algebra. He has also been working on Penalized Complexity priors and the analysis of point patterns with INLA and SPDEs.





Finn Lindgren is a Chair of Statistics in the School of Mathematics at the University of Edinburgh, Scotland. His research covers spatial stochastic modeling and associated computational methods, including applications in climate science, ecology, medical statistics, geosciences, and general environmetrics. He developed the core methods and code for the SPDE interface of the R-INLA package, is a co-developer of the related packages "excursions" and "inlabru," and has given lecture series and practical workshops on spatial modeling with INLA.





Havard Rue is a Professor of Statistics, at the CEMSE Division at the King Abdullah University of Science and Technology, Saudi Arabia, where he leads a research group on Bayesian Computational Statistics & Modeling. He is the main developer of the INLA methodology and the R-INLA Project.
Elias T. Krainski is a Professor Adjunto in the Department of Statistics, Universidade Federal do Parana (Curitiba, Brazil). He has been working on new space-time models and applications in epidemiology and fisheries with INLA and SPDE.





Virgilio Gomez-Rubio is an Associate Professor in the Department of Mathematics, Universidad de Castilla-La Mancha (Albacete, Spain). His research interests are on Bayesian inference, spatial statistics and computational statistics. He has also developed several packages for the R language on spatial data analysis and Bayesian computation.





Haakon Bakka is a Post Doctoral Fellow at the King Abdullah University of Science and Technology. He has given many courses in both INLA and the SPDE approach, and parts of his research on spatial models are included in this book.





Amanda Lenzi is a Post-Doctoral Fellow at the King Abdullah University of Science and Technology in Saudi Arabia, where she is part of the Spatio-Temporal Statistics and Data Science Group. Her research interest is on spatial and spatio-temporal statistics with applications in environmental science, especially in wind energy.





Daniela Castro-Camilo is a Post-Doctoral Fellow working in the Extreme Statistics Research Group at the King Abdullah University of Science and Technology, in Saudi Arabia. Her research interest is on the theory and applications of multivariate and spatial extremes, with a particular focus in environmental applications.





Daniel Simpson is an Assistant Professor in the Department of Statistical Sciences, University of Toronto. His research interests are on Computational Statistics, Spatial Statistics, Bayesian Statistics, and Numerical Linear Algebra. He has also been working on Penalized Complexity priors and the analysis of point patterns with INLA and SPDEs.





Finn Lindgren is a Chair of Statistics in the School of Mathematics at the University of Edinburgh, Scotland. His research covers spatial stochastic modeling and associated computational methods, including applications in climate science, ecology, medical statistics, geosciences, and general environmetrics. He developed the core methods and code for the SPDE interface of the R-INLA package, is a co-developer of the related packages "excursions" and "inlabru," and has given lecture series and practical workshops on spatial modeling with INLA.





Havard Rue is a Professor of Statistics, at the CEMSE Division at the King Abdullah University of Science and Technology, Saudi Arabia, where he leads a research group on Bayesian Computational Statistics & Modeling. He is the main developer of the INLA methodology and the R-INLA Project.

Preamble



What this book is and isn't



<ol><li>


</li><li>The Integrated Nested Laplace Approximation and the R-INLA package </li><li>


Introduction


The INLA method


A simple example


Additional arguments and control options


Manipulating the posterior marginals


Advanced features

<b>
</b>



</li><li><b>Introduction to spatial modeling </b></li><li>


<b>Introduction </b>


<b>The SPDE approach </b>


<b>A toy example </b>


<b>Projection of the random field </b>


<b>Prediction </b>


<b>Triangulation details and examples </b>


<b>Tools for mesh assessment </b>


<b>Non-Gaussian response: Precipitation in Parana </b>

<b><b>
</b></b>



</li><li><b><b>More than one likelihood </b></b></li><li>


<b><b>Coregionalization model </b></b>


<b><b>Joint modeling: Measurement error model </b></b>


<b><b>Copying part of or the entire linear predictor </b></b>

<b><b><b>
</b></b></b>



</li><li><b><b><b>Point processes and preferential sampling </b></b></b></li><li>


<b><b><b>Introduction </b></b></b>


<b><b><b>Including a covariate in the log-Gaussian Cox process </b></b></b>


<b><b><b>Geostatistical inference under preferential sampling </b></b></b>

<b><b><b><b>
</b></b></b></b>



</li><li><b><b><b><b>Spatial non-stationarity </b></b></b></b></li><li>


<b><b><b><b>Explanatory variables in the covariance </b></b></b></b>


<b><b><b><b>The Barrier model </b></b></b></b>


<b><b><b><b>Barrier model for noise data in Albacete (Spain) </b></b></b></b>


<b><b><b><b><b>
</b></b></b></b></b>


</li><li><b><b><b><b><b>Risk assessment using non-standard likelihoods </b></b></b></b></b></li><li>


<b><b><b><b><b>Survival analysis </b></b></b></b></b>


<b><b><b><b><b>Models for extremes </b></b></b></b></b>

<b><b><b><b><b><b>
</b></b></b></b></b></b>



</li><li><b><b><b><b><b><b>Space-time models </b></b></b></b></b></b></li><li>


<b><b><b><b><b><b>Discrete time domain </b></b></b></b></b></b>


<b><b><b><b><b><b>Continuous time domain </b></b></b></b></b></b>


<b><b><b><b><b><b>Lowering the resolution of a spatio-temporal model </b></b></b></b></b></b>


<b><b><b><b><b><b>Conditional simulation: Combining two meshes </b></b></b></b></b></b>

<b><b><b><b><b><b><b>
</b></b></b></b></b></b></b>



</li><li><b><b><b><b><b><b><b>Space-time applications </b></b></b></b></b></b></b></li><li>

</li></ol>

<b><b><b><b><b><b><b>Space-time coregionalization model </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Dynamic regression example </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Space-time point process: Burkitt example </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Large point process dataset </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Accumulated rainfall: Hurdle Gamma model </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b><b>
</b></b></b></b></b></b></b></b>

<b><b><b><b><b><b><b><b>List of symbols and notation </b></b></b></b></b></b></b></b>


<b><b><b><b><b><b><b><b>Packages used in the book </b></b></b></b></b></b></b></b>


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