Spatio temporal statistics
Statistics for spatio-temporal data / Noel Cressie, Christopher K. Wikle. p. cm.—(Wiley series in probability and statistics) Includes bibliographical references and index.Statistics for Spatio-Temporal Data is an excellent book for a graduate-level course on spatio-temporal statistics. It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences. "Spatio-Temporal Statistics with R is the perfect companion to the earlier title by the authors on Statistics for Spatio-Temporal Data. This newest book augments the reader's skillset by showing how to implement a variety of methods to create spatio-temporal graphics and perform data analysis. of spatio‐temporal data have been collected in recent years. How to adequately and rigorously discover latent and significant patterns and knowledge from massive spatio‐temporal data has become a challenge (Han, Kamber, & Tung, 2001). Spatio‐temporal clustering is an important technology of data mining and is primarily aimed at ex‐
Two statistics which measure the degree of correlation in the Discrete Fourier transforms are proposed. These statistics are used to test for spatio-temporal stationarity. It is shown that the same statistics can also be adapted to test for the one-way stationarity (either spatial or temporal stationarity). The proposed methodology is ...
by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Speciﬁcally, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Un-
Jul 11, 2018 · In his seminal book “statistics for spatio-temporal data ”, Cressie et al. characterizes the process of statistical spatio-temporal data analysis in the presence of uncertain and (often) incomplete observations. This work includes prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and ... Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.
spatial-temporal data. Topics include geostatistics, likelihood methods, hierarchical models, Markov random fields, spatial autoregressive models, dynamic spatio-temporal models, and disease mapping. Intended primarily for students in the PhD program in Statistics or Biostatistics. Prerequisites