STATISTICS FOR SPATIO-TEMPORAL DATA PDF
Statistics for Spatio-Temporal Data [Noel Cressie, Christopher K. Wikle] on resourceone.info *FREE* shipping on qualifying offers. Winner of the DeGroot . Statistics for Spatio-Temporal Data has now been reprinted with small corrections to the text and the bibliography. The overall content and pagination of the new. Statistics for Spatio-Temporal Data. Introduction, Visualization, Descriptive Methods. Christopher K. Wikle. University of Missouri. Department of Statistics.
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Request PDF on ResearchGate | On Jan 1, , N.A.C. Cressie and others published Statistics For Spatio-Temporal Data. Statistics for Spatio-Temporal Data. Christopher K. Wikle. Curators' Distinguished Professor. Department of Statistics. University of Missouri. An Introduction to Spatio-Temporal Statistics: Introduction c Christopher K. Wikle . Exploratory Analysis of Spatio-Temporal Data. An Introduction to http://cs. resourceone.info). An Introduction to.
Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros
Along the way, the reader learns the descriptive phase of exploratory analysis and moves on to the dynamic modelling of ST processes. Only then does the reader move onto the rich libraries of R tools available for the model construction.
In the final phase the reader learns how to assess the models he or she has created with the goal of improving them and ultimately choosing the best one.
All this is accomplished using a hands-on approach through lab work that involves complex datasets and the very large library of R packages now available.
Thus, the reader will learn amongst many other things, how to animate their spatial plots of data and the use of Trelliscope for visualizing large ST datasets. For data wrangling, the reader learns about the dyplr and tidyr R packages.
And the reader will master a lot of the skills needed for spatial regression with generalized linear models, Bayesian hierarchical modelling, using the integrated nested Laplace INLA approximation, spatial prediction and future forecasting. Of particular note is the connections the book develops with stochastic partial differential equations and uncertainty quantification, that are developed through discussion of dynamic modelling.
This book will have a prominent place in my reference library.
Zidek, Professor Emeritus, University of British Columbia "This book provides the ideal modern approach to the analysis of spatial-temporal data and implementation of associated models. The theory is laid out clearly by masters of the field and the accompanying R code, packages, and data laboratories both in the text and available online bring the subject to life.
This is not a book to sit on your shelf -- it should be on your desk for ready access and continual use.
Noel Cressie and Christopher K. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. Statistics for Spatio-Temporal Data presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models.
Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes.
Throughout the book, interesting applications demonstrate the relevance of the presented concepts. Vivid, full-color graphics emphasize the visual nature of the topic, and a related FTP site contains supplementary material. Statistics for Spatio-Temporal Data is an excellent book for a graduate-level course on spatio-temporal statistics.
It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
A Spatio-Temporal Model and Inference Tools for Longitudinal Count Data on Multicolor Cell Growth.
This item: Statistics for Spatio-Temporal Data. Chirstopher K. Wikle is a Fellow of the American Statistical Association and the author of more than articles on the topics of spatio-temporal methodology, spatial statistics, hierarchical models, Bayesian methods, and computational methods for large data sets. His work is motivated by problems in climatology, ecology, fisheries and wildlife, meteorology, and oceanography. Request permission to reuse content from this site.
This book is a must for any environmental scientist or engineer engaged in modeling and computation. Clark, H.
Blomquist Professor of Environment, Duke University. This is what I call 'modeling the process, not just the data. Particularly noteable is its extensive coverage not found in any other book in statistical science, of hierarchical dynamic process modeling, a new frontier at the interface between the physical and statistical sciences.
It takes us there with a most-justified excursion into the world of methods such as the extended Kalman filter, sequential importance sampling, and INLA, that address the computational issues confronted at that frontier.
This comprehensive, very readable treatment of hot areas of modern research and applications, is written with great clarity and insight.
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That and its coverage of a broad range of applications, will make it an essential and long-lived reference for statistical as well as non-statistical scientists alike. After extensive surveys of time series analysis and traditional spatial statistics, the authors develop spatio-temporal analysis through a series of chapters covering empirical and exploratory methods, followed by probability models for spatio-temporal processes, and then three chapters on the hierarchical dynamical approach which has been at the core of their own contributions since the late s.
Throughout the book, they develop the methods through detailed descriptions of computational algorithms, leading up to a final chapter that discusses in-depth applications to predicting sea-surface temperatures and wind speeds, remote-sensing measures of atmospheric particles, and bird migration. Every researcher involved in the analysis of large-scale environmental datasets should own a copy of this book.
Undetected country.That and its coverage of a broad range of applications, will make it an essential and long-lived reference for statistical as well as non-statistical scientists alike. Richardson, P. In: Gaber, M.
Andrew Gelman. After all, this is a book not a journal papers.
This book is the most comprehensive overview of spatio-temporal statistics on the market. If you are a seller for this product, would you like to suggest updates through seller support? The authors take a largely Bayesian approach to modeling and analysis and present it clearly and compellingly.