Performance analysis of parameter estimation algorithms based on high‐order moments

International Journal of Adaptive Control and Signal Processing

Volumn 3, Issue 3, 1989, Pages 191-229

  Porat, Boaz ^a   Friedlander, Benjamin ^b  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b FAIRCHILD SEMICONDUCTOR   (United States)

Author keywords

Autoregressive; Cumulants; High order; Moving average; Non Gaussian; Parameter estimation

Indexed keywords

COMPUTER PROGRAMMING--ALGORITHMS; MATHEMATICAL TECHNIQUES; STATISTICAL METHODS--REGRESSION ANALYSIS;

ARMA PROCESSES; CUMULANTS; HIGH-ORDER MOMENTS; NON-GAUSSIAN PROCESSES; PARAMETER ESTIMATION ALGORITHMS;

MATHEMATICAL STATISTICS;

EID: 0024731344     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.4480030302     Document Type: Article

References (24)

1. Cumulants: a powerful tool in signal processing
2. Order reduction of SISO nonminimum phase stochastic systems
3. Identification of non‐minimum phase linear stochastic systems
4. Bispectrum estimation: a parametric approach
5. Identification of non‐minimum phase systems using higher order statistics
6. Bounds on the accuracy of ARMA parameter estimation techniques based on sample covariances
7. ARMA spectral estimation based on partial autocorrelations — Part II: Statistical analysis
8. Instrumental variable methods for ARMA spectral estimation
9. High performance spectral estimation — a new ARMA method
10. Optimal instrumental variable estimates of the AR parameters of an ARMA process
11. Information tradeoffs in using the sample autocorrelation function in ARMA parameter estimation
12. On estimating non‐causal non‐minimum phase ARMA models of non‐gaussian processes
13. The overdetermined recursive instrumental variable method
14. Deconvolution and estimation of transfer function phase and coefficients for non‐Gaussian linear processes
15. On the role of cumulants and Kronecker products for stochastic realization of state‐space and input‐output models