Computationally Efficient Atomic Representations for Nonstationary Stochastic Processes
DOI10.1142/S0219691303000177zbMath1043.62081OpenAlexW2092273390MaRDI QIDQ4474551
Publication date: 12 July 2004
Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219691303000177
nonstationary stochastic processesbest orthogonal basismatching pursuitFrames and overcomplete dictionariesindependent and sparse component analysis
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Inference from stochastic processes (62M99) Numerical methods for wavelets (65T60) Stochastic processes (60G99)
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Cites Work
- High resolution pursuit for feature extraction
- Independent component analysis, a new concept?
- Empirical volatility analysis: Feature detection and signal extraction with function dictionaries
- Atomic Decomposition by Basis Pursuit
- Blind Source Separation by Sparse Decomposition in a Signal Dictionary
- WAVELET TRANSFORMS FOR THE STATISTICAL ANALYSIS OF RETURNS GENERATING STOCHASTIC PROCESSES
- Long-range Dependence: Revisiting Aggregation with Wavelets
- Ten Lectures on Wavelets
- Ideal spatial adaptation by wavelet shrinkage
- Matching pursuits with time-frequency dictionaries
- Computational signal processing with wavelets
- Sparse components of images and optimal atomic decompositions
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