Time Series

Data Analysis and Theory

AvDavid R. Brillinger

Häftad, Engelska, 2001

Del i serien Classics in Applied Mathematics

907 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2001-09-30
Mått:152 x 229 x 28 mm
Vikt:772 g
Format:Häftad
Språk:Engelska
Serie:Classics in Applied Mathematics
Antal sidor:560
Förlag:Society for Industrial & Applied Mathematics,U.S.
ISBN:9780898715019

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Recensioner i media

'Intended for students and researchers, this text employs basic techniques of univariate and multivariate statistics for the analysis of time series and signals. It covers a broad collection of theorems. The techniques are illustrated by data analyses and are discussed both heuristically and formally to serve both the applied and the theoretical worker.' IEEE Signal Processing Magazine

Innehållsförteckning

Preface to the Classics EditionPreface to the Expanded EditionPreface to the First EditionChapter 1: The Nature of Time Series and Their Frequency AnalysisChapter 2: FoundationsChapter 3: Analytic Properties of Fourier Transforms and Complex MatricesChapter 4: Stochastic Properties of Finite Fourier TransformsChapter 5: The Estimation of Power SpectraChapter 6: Analysis of a Linear Time Invariant Relation Between a Stochastic Series and Several Deterministic SeriesChapter 7: Estimating the Second-Order Spectra of Vector-Valued SeriesChapter 8: Analysis of a Linear Time Invariant Relation Between Two Vector-Valued Stochastic SeriesChapter 9: Principal Components in the Frequency DomainChapter 10: The Canonical Analysis of Time SeriesProofs of TheoremsReferencesNotation IndexAuthor IndexSubject IndexAddendum: Fourier Analysis of Stationary Processes.