Statistics for Spatio-Temporal Data Noel Cressie, Christopher K. Wikle
Publisher: Wiley

This high-tech progress produces statistical units sampled over finer and finer grids. Boundaries of spatial units may evolve across time and that adds another layer of mismatches to a spatio-temporal level. This framework is designed to analyze spatio-temporal data produced in several scientific domains. Department name when degree awarded. Job Duties (i) Develop and validate multivariate statistical models of spatiotemporal renewable energy fields, based on data sets of disparate spatiotemporal resolution and extent. My main focus of research is in mathematical statistics and applied probability, particularly in relation to spatial data sets and computational problems as covered in the research areas known as spatial statistics, stochastic geometry, simulation- based inference, Markov chain Monte Carlo methods, and perfect simulation. Radius of gyration, root mean square deviation (RMSD)) to identify similar 3D conformations in folding trajectories. Datasets, while monitoring devices are becoming ever more sophisticated. We extend the spatio-temporal data mining framework that we have developed earlier to analyze and manage such data [5]. Thesis Most of my recent books and papers deal with statistical inference and computational methods for spatial and spatio-temporal point processes. Hierarchical spatial, temporal, and spatio-temporal models allow for the simultaneous modeling of both first and second order processes, thus accounting for underlying autocorrelation in the system while still providing insight into overall Based on preliminary analysis, the data appeared to be overdispersed, containing a disproportionately high number of zeros along with a high variance relative to the mean. Previously, researchers have examined several summary statistics (e.g.