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Time Series with Mixed Spectra

Time Series with Mixed Spectra

Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject. Filling this void, Time Ser... read full description below.

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ISBN 9781138374959
Barcode 9781138374959
Published 28 September 2018 by Taylor & Francis Ltd
Format Paperback
Alternate Format(s) View All (2 other possible title(s) available)
Author(s) By Li, Ta-Hsin
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Full details for this title

ISBN-13 9781138374959
ISBN-10 1138374954
Stock Available
Status In stock at publisher; ships 6-12 working days
Publisher Taylor & Francis Ltd
Imprint CRC Press
Publication Date 28 September 2018
International Publication Date 12 June 2019
Publication Country United Kingdom United Kingdom
Format Paperback
Author(s) By Li, Ta-Hsin
Category Sound, Vibration & Waves (Acoustics)
Technology: General Issues
Dynamics & Vibration Of Materials
Electrical Engineering
Electronics Engineering
Communications Engineering / Telecommunications
Personal Computers
Number of Pages 680
Dimensions Width: 159mm
Height: 235mm
Weight 1,260g
Interest Age General Audience
Reading Age General Audience
Library of Congress Time-series analysis, Spectrum analysis
NBS Text Physics
ONIX Text General/trade;College/higher education;Professional and scholarly
Dewey Code 519.54
Catalogue Code Not specified

Description of this Book

Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject. Filling this void, Time Series with Mixed Spectra focuses on the methods and theory for the statistical analysis of time series with mixed spectra. It presents detailed theoretical and empirical analyses of important methods and algorithms. Using both simulated and real-world data to illustrate the analyses, the book discusses periodogram analysis, autoregression, maximum likelihood, and covariance analysis. It considers real- and complex-valued time series, with and without the Gaussian assumption. The author also includes the most recent results on the Laplace and quantile periodograms as extensions of the traditional periodogram. Complete in breadth and depth, this book explains how to perform the spectral analysis of time series data to detect and estimate the hidden periodicities represented by the sinusoidal functions. The book not only extends results from the existing literature but also contains original material, including the asymptotic theory for closely spaced frequencies and the proof of asymptotic normality of the nonlinear least-absolute-deviations frequency estimator.

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Awards, Reviews & Star Ratings

NZ Review It masterfully integrates the most significant advances in the literature. -Journal of the American Statistical Association ... an excellent introduction and overview of the literature dealing with statistical inference on time-series involving sinusoids. It will be an indispensable reference that research workers and graduate students of allied fields will rely on in the future. -Mathematical Reviews, January 2015 It is extremely thorough in its approach. Every term is carefully defined, and many proofs are given in elaborate detail. ... The range of problems and methods considered in the book is extensive. -Journal of Time Series Analysis, 2015

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Author's Bio

Ta-Hsin Li is a research statistician at the IBM Watson Research Center. He was previously a faculty member at Texas A&M University and the University of California, Santa Barbara. Dr. Li is a fellow of the American Statistical Association and an elected senior member of the Institute of Electrical and Electronic Engineers. He is an associate editor for the EURASIP Journal on Advances in Signal Processing, the Journal of Statistical Theory and Practice, and Technometrics. He received a Ph.D. in applied mathematics from the University of Maryland.

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