Title :
Wavelets as a regularization technique for spectral density estimation
Author_Institution :
Bell Commun. Res., Morristown, NJ, USA
Abstract :
Estimation of the spectral density S(f) of a stationary random process can be viewed as a nonparametric statistical estimation problem. A nonparametric approach based on a wavelet representation for the logarithm of the unknown S(f) is introduced. This approach offers the ability to capture significant components of S(f) at different resolution levels by application of a significance test, and guarantees nonnegativity of the spectral density estimator
Keywords :
random processes; spectral analysis; statistical analysis; wavelet transforms; logarithm; nonparametric statistical estimation; regularization technique; significance test; spectral density estimation; spectral density estimator; stationary random process; wavelet representation; wavelets; Additive noise; Maximum likelihood estimation; Probability density function; Random processes; Random variables; Signal resolution; Testing;
Conference_Titel :
Time-Frequency and Time-Scale Analysis, 1992., Proceedings of the IEEE-SP International Symposium
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-0805-0
DOI :
10.1109/TFTSA.1992.274231
