Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Aims to fill some of this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets.
Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Aims to fill some of this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Marco A. R. Ferreira is a Professor in the Department of Statistics at Virginia Tech. Marco has served the statistics profession in editorial boards of multiple scientific journals including the journal Bayesian Analysis, in several committees of the International Society for Bayesian Analysis and the American Statistical Association, as well as in scientific committees of numerous domestic and international conferences. Marco's current research areas include dynamic models for time series and spatiotemporal data, multiscale models, objective Bayesian methods, stochastic search algorithms, and statistical computation. Major areas of application include bioinformatics, economics, epidemiology, and environmental science. Marco's research has been funded by grants from industry, the National Science Foundation, and the National Institute of Health. Marco has published important scientific papers in top journals such as the Journal of the American Statistical Association, the Journal of the Royal Statistical Society, Biometrika, and Bayesian Analysis. At the time of this writing, Marco has advised over 15 Ph.D. students and postdocs who work in academic, industrial, and governmental positions.
Inhaltsangabe
1. Proper Gaussian Markov Random Fields. 2. Gaussian Spatial Hierarchical Models with ICAR Priors. 3. Objective Priors for Spatio Temporal Models. 4. Spatio Temporal Models for Poisson Areal Data. 5. Dynamic Multiscale Spatio Temporal Thresholding. 6. Multiscale Spatio Temporal Data Assimilation. 7. Multiscale Heteroscedastic Multivariate Spatio Temporal Models. 8. A Model Selection Paradox with Implications to Multiscale Modeling. 9. Ensembles of Dynamic Multiscale Spatio Temporal Models.
