Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
Published on 7. December 2018
298 pages
978-0-429-62821-4 (ISBN)
Description
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.
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.
Reviews / Votes
"Besides the epidemiological perspective, they have also tried to address many of the applied issues in disease mapping practice, which may make this book different from others previously published on this topic. To further facilitate understanding, the authors have made all code and data used in their examples available. A GitHub repository has also been created for hosting the book's online supplementary material. Thus, one can make use of the functionalities that GitHub deploys for their repositories, such as highlighting points to be clarified by the authors. As a great novelty of the book, the online material may enable readers to have direct access to most of the statistical/computing details that there is not enough room to fully explain within the book." ~Sada Nand Dwivedi, ICSB NewsMore details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
File size
7,72 MB
ISBN-13
978-0-429-62821-4 (9780429628214)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Elias Krainski | Virgilio Gomez-Rubio | Haakon Bakka
Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
Book
06/2020
1st Edition
Chapman & Hall/CRC
€75.20
Shipment within 15-20 days
Elias Krainski | Virgilio Gomez-Rubio | Haakon Bakka
Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
Book
12/2018
1st Edition
CRC Press
€170.84
Shipment within 10-20 days
Persons
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.
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.
Content
1. The Integrated Nested Laplace Approximation. 2. Continuous spatial processes. 3. Non-Gaussian observations and covariates in the covariance. 4. Manipulating the random field and more than one likelihood. 5. Log-Cox point process and preferential sampling. 6. Space-time models. 7. Space-time models with different meshes.
