Time Series with Mixed Spectra

Ta-Hsin Li (Autor)

Buch | Softcover

680 Seiten

2019
CRC Press (Verlag)
978-1-138-37495-9 (ISBN)

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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.

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.

Introduction. Basic Concepts. Cramér-Rao Lower Bound. Autocovariance Function. Linear Regression Analysis. Fourier Analysis Approach. Estimation of Noise Spectrum. Maximum Likelihood Approach. Autoregressive Approach. Covariance Analysis Approach. Further Topics. Appendix. Bibliography.

Erscheinungsdatum	18.06.2019
Zusatzinfo	19 Tables, black and white; 105 Illustrations, black and white
Verlagsort	London
Sprache	englisch
Maße	156 x 234 mm
Gewicht	453 g
Themenwelt	Naturwissenschaften ► Physik / Astronomie ► Mechanik
	Technik ► Maschinenbau
	Technik ► Nachrichtentechnik
ISBN-10	1-138-37495-4 / 1138374954
ISBN-13	978-1-138-37495-9 / 9781138374959
Zustand	Neuware