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

Product 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 Matérn 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 modelsAll 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. Review "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 News About the Author Elias T. Krainski is a Professor Adjunto in the Department of Statistics, Universidade Federal do Paraná (Curitiba, Brazil). He has been working on new space-time models and applications in epidemiology and fisheries with INLA and SPDE.Virgilio Gómez-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 Technol

Simpson, Daniel

Gómez-Rubio, Virgilio

Rue, Håvard

Krainski, Elias

Bakka, Haakon

Lenzi, Amanda

Castro-Camilo, Daniela

Lindgren, Finn

Reference

Chapman and Hall/CRC

Applied Spatial Data Analysis with R

Bivand, Roger S.

Pebesma, Edzer J.

Springer

Bayesian inference with INLA

The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed.\nBayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website.\nThis book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.

Science & Math

Biological Sciences

The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.

Routledge

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 Matérn 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 first presents R packages, functions, classes and methods for handling spatial data. It then showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics and disease mapping.

Applied Spatial Data Analysis with R (Use R!, 10)

Pebesma, Edzer

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