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The Dantzig selector: Statistical estimation when p is much larger than n

The Dantzig selector: Statistical estimation when p is much larger than n,10.1214/009053606000001523,Emmanuel Candes,Terence Tao

The Dantzig selector: Statistical estimation when p is much larger than n   (Citations: 389)
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Published in 2007.
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    • ...The DS problem is a model recently proposed by Candès and Tao 11 for recovering large sparse signal using a relatively small number of linear measurements or observations...

    Zhaosong Lu. Primal–dual first-order methods for a class of cone programming

    • ...Note that the Dantzig Selector (Candes and Tao 2007; James, Radchenko, and Dasso 2009) can be put into this confidence region framework as it is exactly in the form of (<0003" ref-type="disp-formula">2...

    Funda Guneset al. A Confidence Region Approach to Tuning for Variable Selection

    • ...These conditions are mainly used to address the worst-case performance of sparse recovery [3]–[5]...
    • ...In this letter, we investigate the quality of a sensing matrix with respect to the mean squared error (MSE) performance of the oracle estimator [3], whose performance has been shown to act as a benchmark to the performance of various common sparse recovery algorithms...
    • ...Consequently, to avoid the analysis of a single or several practical sparse recovery algorithms such as the basis pursuit denoise (BPDN), the Dantzig selector, and orthogonal matching pursuit (OMP), we capitalize on the well-known oracle estimator that performs ideal least square (LS) estimation based on prior knowledge of the sparse vector support [3]...
    • ...The oracle estimator MSE incurred in the estimation of a sparse deterministic vector in the presence of a standard Gaussian noise vector , according to the model in (1), is given by [3]...

    Wei Chenet al. On the Use of Unit-Norm Tight Frames to Improve the Average MSE Perfor...

    • ...Available computational algorithms include, but are not limited to, solution path algorithms 27, interior-point method 28, linear programming 10 17, l 1-constrained quadratic programming 25, and one-step local linear approximation 29...

    Tong Tong Wu. Lasso penalized semiparametric regression on high-dimensional recurren...

    • ...the elastic net (Zou and Hastie, 2005), SCAD (Fan and Li, 2001), the Dantzig selector (Candes and Tao, 2007) and boosting approaches (for example Bühlmann and Yu, 2003)...
    • ...Comparable procedures have already been proposed, for example, by Efron et al. (2004) under the name "Lars-OLS hybrid", or by Candes and Tao (2007) as "Gauss-Dantzig Selector"...

    Jan Gertheisset al. Sparse modeling of categorial explanatory variables

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