Above are papers related to the following paper: [Paper_ID] Paper #633031. 	[Title] WaveShrink: Shrinkage Functions and Thresholds [Abstract] Donoho and Johnstone's WaveShrink procedure has proven valuable for signal de-noising and non-parametric regression. WaveShrink is based on the principle of shrinking wavelet coefficients towards zero to remove noise. WaveShrink has very broad asymptotic near-optimality properties. In this paper, we introduce a new shrinkage scheme, semisoft, which generalizes hard and soft shrinkage. We study the properties of the shrinkage functions, and demonstrate that semisoft shrinkage offers advantages over both hard shrinkage (uniformly smaller risk and less sensitivity to small perturbations in the data) and soft shrinkage (smaller bias and overall L 2 risk). We also construct approximate pointwise confidence intervals for WaveShrink and address the problem of threshold selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #633030. 	[Title] Understanding WaveShrink: Variance and Bias Estimation [Abstract] Research Report Please summarise all the given information.

Paper #633031 has the title `WaveShrink: Shrinkage Functions and Thresholds  
` and the abstract `Donoho and Johnstone's WaveShrink procedure has proven valuable for signal de-noising and non-parametric regression. WaveShrink is based on the principle of shrinking wavelet coefficients towards zero to remove noise. WaveShrink has very broad asymptotic near-optimality properties. In this paper, we introduce a new shrinkage scheme, semisoft, which generalizes hard and soft shrinkage. We study the properties of the shrinkage functions, and demonstrate that semisoft shrinkage offers advantages over both hard shrinkage (uniformly smaller risk and less sensitivity to small perturbations in the data) and soft shrinkage (smaller bias and overall L 2 risk). We also construct approximate pointwise confidence intervals for WaveShrink and address the problem of threshold selection. 
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Question: Which category should Paper #633031 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #633031 has the title `WaveShrink: Shrinkage Functions and Thresholds  
` and the abstract `Donoho and Johnstone's WaveShrink procedure has proven valuable for signal de-noising and non-parametric regression. WaveShrink is based on the principle of shrinking wavelet coefficients towards zero to remove noise. WaveShrink has very broad asymptotic near-optimality properties. In this paper, we introduce a new shrinkage scheme, semisoft, which generalizes hard and soft shrinkage. We study the properties of the shrinkage functions, and demonstrate that semisoft shrinkage offers advantages over both hard shrinkage (uniformly smaller risk and less sensitivity to small perturbations in the data) and soft shrinkage (smaller bias and overall L 2 risk). We also construct approximate pointwise confidence intervals for WaveShrink and address the problem of threshold selection. 
`.
Question: Which category should Paper #633031 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods