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Wavelet Thresholding Techniques for Power Spectrum Estimation
Pierre Moulin
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Dive into the research topics of 'Wavelet Thresholding Techniques for Power Spectrum Estimation'. Together they form a unique fingerprint.
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Keyphrases
Estimation Problem
100%
Wavelet Coefficients
100%
Threshold Method
100%
Power Spectrum Estimation
100%
Wavelet Thresholding
100%
Power Spectrum
50%
Statistical Estimation
50%
Independent Random Variables
50%
Additive Noise
50%
Spectral Estimation
50%
Resolution Level
50%
Non-parametric Approach
50%
Additive White Gaussian Noise
50%
Resolution Requirements
50%
Saddlepoint Approximation
50%
Non-negativity
50%
Wavelet Representation
50%
Noise-resolution Tradeoff
50%
Nonparametric Statistical
50%
Stationary Random Process
50%
Noise Coefficient
50%
Computer Science
Wavelet Coefficient
100%
Power Spectrum Estimation
100%
Approximation (Algorithm)
50%
Statistical Estimation
50%
Random Variable
50%
Resolution Noise
50%
Resolution Level
50%
Wavelet Representation
50%
Non-Gaussian Noise
50%
Noise Coefficient
50%
Mathematics
Wavelet
100%
Power Spectra
100%
Wavelet Thresholding
100%
Independent Random Variables
33%
Additive Noise
33%
Saddle Point Approximation
33%
Nonnegativity
33%
Stationary Random Process
33%
Random Noise
33%