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Time series --- Autocorrelation functions --- Random variables --- Stochastic processes

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"A severe thunderstorm morphs into a tornado that cuts a swath of destruction through Oklahoma. How do we study the storm's mutation into a deadly twister? Avian flu cases are reported in China. How do we characterize the spread of the flu, potentially preventing an epidemic? The way to answer important questions like these is to analyze the spatial and temporal characteristics--origin, rates, and frequencies--of these phenomena. This comprehensive text introduces advanced undergraduate students, graduate students, and researchers to the statistical and algebraic methods used to analyze spatiotemporal data in a range of fields, including climate science, geophysics, ecology, astrophysics, and medicine. Gidon Eshel begins with a concise yet detailed primer on linear algebra, providing readers with the mathematical foundations needed for data analysis. He then fully explains the theory and methods for analyzing spatiotemporal data, guiding readers from the basics to the most advanced applications. This self-contained, practical guide to the analysis of multidimensional data sets features a wealth of real-world examples as well as sample homework exercises and suggested exams"--

Spatial analysis (Statistics) --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- EOF analysis. --- EOF. --- GramГchmidt orthogonalization. --- SVD analysis. --- SVD. --- astrophysics. --- autocorrelation functions. --- autocovariance. --- autoregressive model. --- climate science. --- column space. --- covariability matrix. --- data analysis. --- data matrices. --- degrees of freedom. --- deterministic science. --- ecology. --- eigen-decomposition. --- eigen-techniques. --- eigenanalysis. --- eigenvalues. --- empirical orthogonal functions. --- empirical science. --- empiricism. --- exercises. --- forward problem. --- geophysics. --- inverse problem. --- linear algebra. --- linear regression. --- matrices. --- matrix structure. --- matrix. --- medicine. --- multidimensional data sets. --- multidimensional data. --- nondeterministic phenomena. --- null space. --- phenomena. --- probability distribution. --- row space. --- singular value decomposition. --- spatiotemporal data. --- spectral representation. --- square matrices. --- statistics. --- stochastic processes. --- subjective decisions. --- theoretical science. --- time series. --- timescale. --- tornado. --- variables. --- vectors.