Name
Abstract | ||
|---|---|---|
A multistep iterative and fast recursive algorithm, for autoregressive moving average, (ARMA) spectral estimation is presented. The AR parameters of an ARMA process are estimated using the extended instrumental variable (EIV) method. The optimal choice of instruments, prefilter, and weighting matrix is investigated. A bootstrapping procedure that has computational convenience is proposed for the algorithm. The statistical analysis and experiments show that the optimal IV estimate is unbiased, consistent, efficient, asymptotically normal, and equivalent to the maximum-likelihood (ML) estimate and the prediction error (PE) estimate; and the proposed algorithm has the advantages of sharper resolution, less frequency bias, and better efficiency of convergence |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1109/78.107422 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
prediction error estimate,parameter estimation,statistical analysis,prefilter,weighting matrix,maximum likelihood estimate,spectral analysis,multistep iterative algorithm,convergence,bootstrapping procedure,resolution,autoregressive parameters,extended instrumental variable,frequency bias,fast recursive algorithm,autoregressive moving average,arma spectral estimation,iterative methods,optimal instrumental variable method,maximum likelihood estimation,recursive algorithm,moving average,maximum likelihood,spectral estimation,prediction error,lattices,algorithm design and analysis,instrumental variable | Autoregressive–moving-average model,Mathematical optimization,Weighting,Spectral density estimation,Recursion (computer science),Iterative method,Bootstrapping,Instrumental variable,Estimation theory,Mathematics | Journal |
Volume | Issue | ISSN |
39 | 12 | 1053-587X |
Citations | PageRank | References |
3 | 0.88 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei Guo Zou | 1 | 3 | 0.88 |
| Lian Shi Du | 2 | 3 | 0.88 |