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.

About the Author

David R. Brillinger is a Professor in the Department of Statistics at the University of California, Berkeley, and is also chair of the Bernoulli Society's Committee on Probability and Statistics in the Physical Sciences. A native Canadian, he is President of the Statistical Society of Canada for the year 2001-2002. Dr. Brillinger is the recipient of many awards in mathematics and statistics, has edited and served on the editorial boards of several distinguished journals, and is a frequent invited lecturer.

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