Ta-Hsin Li

Time Series with Mixed Spectra (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book 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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 680
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781420010060
• Artikelnr.: 40067690

Autorenporträt

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.

Inhaltsangabe

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.