Title :
Almost sure convergence analysis of autoregressive spectral estimation in additive noise
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, CA, USA
fDate :
1/1/1991 12:00:00 AM
Abstract :
The almost sure convergence properties of autoregressive spectral estimates from noisy observations are derived. Sharp rates of almost sure convergence are established for the estimates of the autoregressive parameters, innovation variance, and spectral density function of the signal process. The distributions of the signal and noise processes are arbitrary
Keywords :
convergence; parameter estimation; random noise; signal processing; spectral analysis; ARMA; additive noise; almost sure convergence properties; autoregressive spectral estimation; innovation variance; parameter estimation; signal process; spectral density function; Additive noise; Biomedical signal processing; Convergence; Density functional theory; Random variables; Signal processing; Signal resolution; Spectral analysis; Technological innovation; Yield estimation;
Journal_Title :
Information Theory, IEEE Transactions on
