Parametric polyspectrum density estimation using the bootstrap method

Authors:
Shahnoor Shanta;Visakan Kadirkamanathan
Affiliations:
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK;Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
Venue:
Signal Processing
Year:
2007

Citing 3
Cited 0

System identification: theory for the user

System identification: theory for the user
Bootstrapping techniques in the estimation of higher-order cumulants from short data records

ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Bootstrapping bispectra: an application to testing for departurefrom Gaussianity of stationary signals

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

A method for obtaining the statistical distribution of parametric polyspectra when applied to a single set of short data record is presented. The method applies model-based bootstrap to a time-series data to obtain the statistical distribution of the coefficients of the approximating process of autoregressive moving average (ARMA) type with known order (p, q). As the polyspectral estimate depend on these coefficients, relevant statistical information of the estimated polyspectrum is obtained from the distribution function of the coefficients. Examples of a simulated and real data are provided to illustrate the method.