Time Series with Mixed Spectra

Name: Time Series with Mixed Spectra
Brand: Taylor & Francis
SKU: 9781584881766
Price: 15904.0 INR
Availability: InStock

by Ta-Hsin Li

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ISBN13: 9781584881766
Binding: Hardcover
Subject: N/A
Publisher: Taylor & Francis
Publisher Imprint: CRC Press
Publication Date: January 15, 2019
Pages: 680
Original Price: GBP 187.99
Language: English
Edition: N/A
Item Weight: 1080 grams
BISAC Subject(s): Probability & Statistics / Bayesian Analysis and Electrical

About The 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.

About The Author

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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