On The Use Of Second- And Higher-order Inverse Statistics
- 24 August 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Inverse higher-order statistics of non-Gaussian, stationary random processes are introduced in this paper, as an extension of their 2nd- order counterparts, known as inverse correlations. Their use in system identification, and specifically in model order determination and parameter estimation problems, is investigated. Estimation procedures are proposed for obtaining sample estimates of inverse statistics and the corresponding (poly)spectra. The algorithms derived are illustrated by simulation examples, involving inverse 2nd- and 3rd-order statistics.Keywords
This publication has 15 references indexed in Scilit:
- Optimal estimates of MA and ARMA parameters of non-Gaussian processes from high-order cumulantsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Signal reconstruction from multiple correlations: frequency- and time-domain approachesJournal of the Optical Society of America A, 1989
- Higher-order spectrum estimation via noncausal autoregressive modeling and deconvolutionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Bispectrum estimation: A digital signal processing frameworkProceedings of the IEEE, 1987
- An Introduction to Bispectral Analysis and Bilinear Time Series ModelsPublished by Springer Nature ,1984
- Phase estimation using the bispectrumProceedings of the IEEE, 1984
- Deconvolution and Estimation of Transfer Function Phase and Coefficients for Nongaussian Linear ProcessesThe Annals of Statistics, 1982
- Inverse AutocorrelationsJournal of the Royal Statistical Society. Series A (General), 1979
- Some recent advances in time series modelingIEEE Transactions on Automatic Control, 1974
- The Inverse Autocorrelations of a Time Series and Their ApplicationsTechnometrics, 1972