Modeling Spatio-Temporal Data

<p>Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. <strong>Modeling Spatio-Temporal Data: Markov Random Fields Objectives Bayes and Multiscale Models</strong> aims to fill this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets including proper Gaussian Markov random fields dynamic multiscale spatio-temporal models and objective priors for spatial and spatio-temporal models. The goal is to make these approaches more accessible to practitioners and to stimulate additional research in these important areas of spatial and spatio-temporal statistics.</p><p><strong>Key topics:</strong></p><ul> <li>Proper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models.</li> <li>Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects including reference priors results on fast computations and objective Bayes model selection.</li> <li>Objective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects.</li> <li>Spatio-temporal models based on proper Gaussian Markov random fields for Poisson observations.</li> <li>Dynamic multiscale spatio-temporal thresholding for spatial clustering and data compression.</li> <li>Multiscale spatio-temporal assimilation of computer model output and monitoring station data.</li> <li>Dynamic multiscale heteroscedastic multivariate spatio-temporal models.</li> <li>The M-open multiple optima paradox and some of its practical implications for multiscale modeling.</li> <li>Ensembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes.</li> </ul><p>The audience for this book are practitioners researchers and graduate students in statistics data science machine learning and related fields. Prerequisites for this book are master's-level courses on statistical inference linear models and Bayesian statistics. This book can be used as a textbook for a special topics course on spatial and spatio-temporal statistics as well as supplementary material for graduate courses on spatial and spatio-temporal modeling.</p>