Asymptotic Theory of Mixed Time Averages and Kth-Order Cyclic-Moment and Cumulant Statistics

Amod V. Dandawaté, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

202 Scopus citations

Abstract

We generalize Parzen's analysis of “asymptotically stationary” processes to mixtures of deterministic, stationary, nonstationary, and generally complex time series. Under certain mixing conditions expressed in terms of joint cumulant summability, we show that time averages of such mixtures converge in the mean-square sense to their ensemble averages. We additionally show that sample averages of arbitrary orders are jointly complex normal and provide their covariance expressions. These conclusions provide us with statistical tools that treat random and deterministic signals on a common framework and are helpful in defining generalized moments and cumulants of mixed processes. As an important consequence, we develop consistent and asymptotically normal estimators for time-varying, and cyclic-moments and cumulants of kth-order cyclostationary processes and provide computable variance expressions. Some examples are considered to illustrate the salient features of the analysis.

Original languageEnglish (US)
Pages (from-to)216-232
Number of pages17
JournalIEEE Transactions on Information Theory
Volume41
Issue number1
DOIs
StatePublished - Jan 1995

Bibliographical note

Funding Information:
Manuscript received May 3, 1993; revised May 10, 1994. Parts of the results were presented at the International Conference on Acoustics, Speech, and Signal Processing, Minneapolis, MN, April 27-31, 1993. This research was funded by the Office of Naval Research under Grant N00014-93-1-0485. The authors are with the Department of Electrical Engineering, University of Virginia, Charlottesville, VA 22903-2442 USA. IEEE Log Number 9406573.

Keywords

  • Cyclostationarity
  • almost periodic time series
  • asymptotic normality
  • consistency
  • cumulants
  • higher order statistics
  • mixed spectra

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