Results 1  10
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220
Simultaneous regression shrinkage, variable selection and clustering of predictors with
 OSCAR, Biometrics
, 2007
"... Summary. Variable selection can be challenging, particularly in situations with a large number of predictors with possibly high correlations, such as gene expression data. In this paper, a new method called the OSCAR (Octagonal Shrinkage and Clustering Algorithm for Regression) is proposed to simult ..."
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Cited by 87 (7 self)
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coefficient. The proposed procedure is shown to compare favorably to the existing shrinkage and variable selection techniques in terms of both prediction error and model complexity, while yielding the additional grouping information.
A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation
 SIAM Journal on Scientific Computing
, 2010
"... Abstract. We propose a fast algorithm for solving the ℓ1regularized minimization problem minx∈R n µ‖x‖1 + ‖Ax − b ‖ 2 2 for recovering sparse solutions to an undetermined system of linear equations Ax = b. The algorithm is divided into two stages that are performed repeatedly. In the first stage a ..."
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Cited by 54 (8 self)
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firstorder iterative method called “shrinkage ” yields an estimate of the subset of components of x likely to be nonzero in an optimal solution. Restricting the decision variables x to this subset and fixing their signs at their current values reduces the ℓ1norm ‖x‖1 to a linear function of x
Shrinkage algorithms for MMSE covariance estimation
 Signal Processing, IEEE Transactions on
, 2010
"... Abstract—We address covariance estimation in the sense of minimum meansquared error (MMSE) when the samples are Gaussian distributed. Specifically, we consider shrinkage methods which are suitable for high dimensional problems with a small number of samples (large p small n). First, we improve on t ..."
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Cited by 29 (2 self)
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Abstract—We address covariance estimation in the sense of minimum meansquared error (MMSE) when the samples are Gaussian distributed. Specifically, we consider shrinkage methods which are suitable for high dimensional problems with a small number of samples (large p small n). First, we improve
The SLEXShrinkage Approach
, 2009
"... We develop a statistical method for discriminating and classifying multivariate nonstationary signals. It is assumed that the processes that generate the signals are characterized by their timeevolving spectral matrix a description of the dynamic connectivity between the time series components. He ..."
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features that best separate classes of time series. The SLEX approach yield readily interpretable results since it is a timedependent analogue of Fourier approach to stationary time series. Moreover, it uses computationally efficient algorithms to enable handling of large data sets. We estimate the SLEX
Numerical Investigation of the Impact of Shrinkage on the Pyrolysis of
"... Keywords:shrinkage, MFIX, numerical simulation, biomass fast pyrolysis Abstract. A new method for modeling shrinkage of a biomass particle is presented with the unreactedcoreshrinking model and shrinking core model for coal combustion. 2D model of biomass pyrolysis in a moving bed was established. ..."
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. The simulation was carried out with open source code MFIX by using EulerianEulerian multiphase model. The results show that shrinkage can reduce the pyrolysis time, promote heat transfer, increase the biomass conversion percent and tar yield and decrease the noncondensable gas and char yield.
A Wavelet Shrinkage Approach to Tomographic Image Reconstruction
 J. Amer. Statist. Assoc
, 1996
"... A method is proposed for reconstructing images from tomographic data with respect to a twodimensional wavelet basis. The WaveletVaguelette Decomposition is used as a framework within which expressions for the necessary wavelet coefficients may be derived. These coefficients are calculated using a ..."
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Cited by 31 (1 self)
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version of the filtered backprojection algorithm, as a computational tool, in a multiresolution fashion. The necessary filters are defined in terms of the underlying wavelets. Denoising is accomplished through an adaptation of the Wavelet Shrinkage approach of Donoho et al., and amounts to a form
Shrinkage of de Morgan formulae under restriction
, 1993
"... It is shown that a random restriction leaving only a fraction " of the input variables unassigned reduces the expected de Morgan formula size of the induced function by a factor of O(" 5\Gamma p 3 2 ) = O(" 1:63 ). (A de Morgan, or unate, formula is a formula over the basis f; ; ..."
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Cited by 14 (6 self)
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; ; :g.) This improves a longstanding result of O(" 1:5 ) by Subbotovskaya and a recent improvement to O(" 21\Gamma p 73 8 ) = O(" 1:55 ) by Nisan and Impagliazzo. The new exponent yields an increased lower bound of n 7\Gamma p 3 2 \Gammao(1) = \Omega\Gamma n 2
Boundary Coiflets For Wavelet Shrinkage In Function Estimation
, 2003
"... There are standard modifications of of certain compactly supported wavelets that yield orthonormal bases on a bounded interval. We extend one such construction to those wavelets, such as `coiflets', that may have fewer vanishing moments than had previously to be assumed. Our motivation lies in ..."
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Cited by 11 (2 self)
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There are standard modifications of of certain compactly supported wavelets that yield orthonormal bases on a bounded interval. We extend one such construction to those wavelets, such as `coiflets', that may have fewer vanishing moments than had previously to be assumed. Our motivation lies
A Family of Shrinkage AdaptiveFiltering Algorithms
, 2013
"... A family of adaptivefiltering algorithms that uses a variable step size is proposed. A variable step size is obtained by minimizing the energy of the noisefree a posteriori error signal which is obtained by using a known minimization formulation. Based on this methodology, a shrinkage affine pro ..."
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Cited by 2 (0 self)
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projection (SHAP) algorithm, a shrinkage leastmeansquares (SHLMS) algorithm, and a shrinkage normalized leastmeansquares (SHNLMS) algorithm are proposed. The SHAP algorithm yields a significantly reduced steadystate misalignment as compared to the conventional affine projection (AP), variable
Compressed Sensing Recovery via Nonconvex Shrinkage Penalties
"... The `0 minimization of compressed sensing is often relaxed to `1, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated non ..."
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Cited by 1 (0 self)
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The `0 minimization of compressed sensing is often relaxed to `1, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated
Results 1  10
of
220