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The Dantzig selector:statistical estimation when p is much larger than n
Statistical linear model model selection ideal estimation oracle inequalities sparse solutions to underdetermined systems `1-minimization linear programming restricted orthonormality geometry in high dimensions random matrices
2015/6/17
In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Xβ + z, where β ∈ Rp is...
REJOINDER: THE DANTZIG SELECTOR:STATISTICAL ESTIMATION WHEN P IS MUCH LARGER THAN N
DANTZIG SELECTOR STATISTICAL ESTIMATION MUCH LARGER THAN N
2015/6/17
First of all, we would like to thank all the discussants for their interest and comments, as well as for their thorough investigation. The comments all underlie the importance and timeliness of the to...
Parallelism, Uniqueness, and Large-Sample Asymptotics for the Dantzig Selector
Lasso,Regularizedregression Variableselectionandestimation.
2012/11/21
The Dantzig selector (Candes and Tao, 2007) is a popular l1-regularization method for variable selection and estimation in linear regression. We present a very weak geometric condition on the observed...
Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
Linear model Lasso Dantzig Sparsity Model selection Sign consistency
2009/9/16
We derive the l∞ convergence rate simultaneously for Lasso and Dantzig estimators in a high-dimensional linear regression model under a mutual coherence assumption on the Gram matrix of the design and...