Time Series: Data Analysis and Theory (Classics in Applied Mathematics, 36) (Classics in Applied Mathematics): David R. Brillinger
SIAM: Society for Industrial and Applied Mathematics | ISBN: 0898715016 | 2001-09-01 | djvu (ocr) | 540 pages | 4.56 Mb
Intended for students and researchers, this text employs basic techniques of univariate and multivariate statistics for the analysis of time series and signals. It provides a broad collection of theorems, placing the techniques on firm theoretical ground. The techniques, which are illustrated by data analyses, are discussed in both a heuristic and a formal manner, making the book useful for both the applied and the theoretical worker. An extensive set of original exercises is included. Time Series: Data Analysis and Theory takes the Fourier transform of a stretch of time series data as the basic quantity to work with and shows the power of that approach. It considers second- and higher-order parameters and estimates them equally, thereby handling non-Gaussian series and nonlinear systems directly. The included proofs, which are generally short, are based on cumulants.
Brillinger's book gives a thorough and advanced treatment of the frequency domain approach to time series analysis. It is more rigorous and advanced than Bloomfield but is not as easy to read and understand. It is the only text that I know of, to illustrate the power of the complex normal distribution as first suggested by N. Roy Goodman. Besides the interesting and rigorous treatment another nice feature of the book is the data analytic approach to many real time series.
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