Discussion of “the Dantzig selector”
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چکیده
منابع مشابه
DISCUSSION : THE DANTZIG SELECTOR : STATISTICAL ESTIMATION WHEN p IS MUCH LARGER THAN
1. Introduction. This is a fascinating paper on an important topic: the choice of predictor variables in large-scale linear models. A previous paper in these pages attacked the same problem using the " LARS " algorithm (Efron, Hastie, Johnstone and Tibshirani [3]); actually three algorithms including the Lasso as middle case. There are tantalizing similarities between the Dantzig Selector (DS) ...
متن کاملDASSO: Connections Between the Dantzig Selector and Lasso
We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig selector with a similar computational cost to the LARS algorithm that is used to compute the Lasso. DASSO efficiently constructs a piecewise linear path through a sequential simplex-like algorithm, which is remarkably similar to LARS. Comparison of the two algorithms sheds new light on the question of how ...
متن کاملDISCUSSION : THE DANTZIG SELECTOR : STATISTICAL ESTIMATION WHEN p IS MUCH LARGER THAN
given just a single parameter t . Two active-set methods were described in [11], with some concern about efficiency if p were large, where X is n× p . Later when basis pursuit de-noising (BPDN) was introduced [2], the intention was to deal with p very large and to allow X to be a sparse matrix or a fast operator. A primal–dual interior method was used to solve the associated quadratic program, ...
متن کاملDantzig selector homotopy with dynamic measurements
The Dantzig selector is a near ideal estimator for recovery of sparse signals from linear measurements in the presence of noise. It is a convex optimization problem which can be recast into a linear program (LP) for real data, and solved using some LP solver. In this paper we present an alternative approach to solve the Dantzig selector which we call “Primal Dual pursuit” or “PD pursuit”. It is...
متن کاملThe Double Dantzig
The Dantzig selector (Candes and Tao, 2007) is a new approach that has been proposed for performing variable selection and model fitting on linear regression models. It uses an L1 penalty to shrink the regression coefficients towards zero, in a similar fashion to the Lasso. While both the Lasso and Dantzig selector potentially do a good job of selecting the correct variables, several researcher...
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تاریخ انتشار 2007