Wavelets as a regularization technique for spectral density estimation

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.<
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