Frequency estimation of undamped exponential signals using genetic algorithms
Computational Statistics & Data Analysis
Autoregressive frequency detection using Regularized Least Squares
Journal of Multivariate Analysis
| Hi-index | 35.68 |
Strong consistency and asymptotic normality are derived for the maximum-likelihood estimates (MLEs) of the unknown parameters (ω 1,. . .,ωp), (α1,. . ., αp), and σ2 in the superimposed exponential model for signals, Yt=Σ α exp (itωk)+et, where the summation is from k=1 to p, t=0, 1, . . ., n-1, and σ2 is the variance of the complex normal distribution of et. As a by-product, it is found that the MLEs of the parameters attain the Cramer-Rao lower bound for the asymptotic covariance matrix