Nonparametric time series analysis for periodically correlated processes

Authors:
H. L. Hurd
Affiliations:
Harry L. Hurd Associates, Raleigh, NC
Venue:
IEEE Transactions on Information Theory
Year:
2006

Citing 0
Cited 5

Component covariance analysis for periodically correlated random processes

Signal Processing
Linear filtration methods for statistical analysis of periodically correlated random processes-Part II: Harmonic series representation

Signal Processing
Linear filtration methods for statistical analysis of periodically correlated random processes-Part I: Coherent and component methods and their generalization

Signal Processing
Non-parametric frequency identification and estimation in mean function for almost periodically correlated time series

Journal of Multivariate Analysis
Component statistical analysis of second order hidden periodicities

Digital Signal Processing

Quantified Score

Hi-index	754.84

Visualization

Abstract

Correlation functions of continuous-time periodically correlated processes can be represented by a Fourier series with coefficient functions. It is shown that the usual estimator for stationary covariances, formed from a single sample path of the process, can be simply modified to provide a consistent (in quadratic mean) estimator for any of the coefficient functions resulting from the aforementioned representation. It is shown that, if the process is Gaussian and B k(τ) is a Fourier integral with respect to a density function gk(λ), a two-dimensional periodogram, formed from a single sample function, can be smoothed along a line of constant difference frequency to provide a consistent estimator for gk(λ). This natural extension of the well-known procedure for stationary processes provides a method for nonparametric spectral analysis of periodically correlated processes