Substantially updated, this self-contained second edition uses Bayesian hierarchical models in the analysis of spatial data. Three new chapters cover hierarchical models for point-process data with a focus on Bayesian inference in the presence of spatial and non-spatial predictors; the modeling of...
To Be Published August 19th 2014 by Chapman and Hall/CRC
Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector-space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis. Each chapter introduces a key topic, such as...
To Be Published June 2nd 2014 by Chapman and Hall/CRC
Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too...
Published December 16th 2003 by Chapman and Hall/CRC