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 stati

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. Cramer-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.