Using R for Bayesian Spatial and SpatioTemporal Health Modeling (Chapman & Hall/CRC The R Series),Used

Using R for Bayesian Spatial and SpatioTemporal Health Modeling (Chapman & Hall/CRC The R Series),Used

In Stock
SKU: SONG0367490129
Brand: CRC Press
$154.25 $220.36
Save $66.11
Quantity
Add to wishlist
Add to compare

Processing time: 1-3 days

US Orders Ships in: 3-5 days

International Orders Ships in: 8-12 days

Return Policy: 15-days return on defective items

Payment Option
Payment Methods

Help

If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.

Customer service

All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com

Sale & Press

If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com

Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and SpatioTemporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.Features:Review of R graphics relevant to spatial health dataOverview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial dataBayesian Computation and goodnessoffitReview of basic Bayesian disease mapping modelsSpatiotemporal modeling with MCMC and INLASpecial topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modelingSoftware for fitting models based on BRugs, Nimble, CARBayes and INLAProvides code relevant to fitting all examples throughout the book at a supplementary websiteThe book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatiotemporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of georeferenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

Recently Viewed