PENALIZED LINEAR UNBIASED SELECTION

By Cun-Hui Zhang. 2007

66 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Stability selection (2008, neutral): “Define furthermore NΛ =N ∩ ŜΛ to be the set of noise variables (in N) which appear in ŜΛ and analogously UΛ = S ∩ ŜΛ.”
Regularization Parameter Selections via Generalized Information Criterion (2010, methodology): “…(i) Fan and Li (2001) proposed the local quadratic approximation (LQA) al-gorithm, which was further analyzed by Hunter and Li (2005); (ii) Efron et al. (2004) introduced the LARS algorithm, which can be used for the adaptive LASSO (Zou 2006; Wang, Li, and Tsai 2007a; Zhang and Lu 2007).”
A Selective Overview of Variable Selection in High Dimensional Feature Space (2009, methodology): “In addition, Zhang (2009) also shows that the solution path b β ( λ ) is piecewise linear in λ , and the proposed solution path has desired statistical properties.”
Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation (2007, background): “See also Zhang (2007) for a general solution to the folded-concave penalized least-squares problem.”
Non-Concave Penalized Likelihood with NP-Dimensionality (2009, methodology): “For the L 1 penalty, SCAD, and MCP, we have κ ( p λ ) = 0, ( a − 1) − 1 , and a − 1 , respectively.”
Sure independence screening for ultrahigh dimensional feature space (2006, unknown): “Then, byV1 − V 2 1 ≤ V1 and Bonferroni’s inequality, (53) follows immediately from (54)–(56).”
VARIABLE SELECTION IN NONPARAMETRIC ADDITIVE MODELS (2010, background): “In other words, any initial estimator satisfying (B1) will ensure that the adaptive group Lasso (based on this initial estimator) is selection consistent, provided that certain regularity conditions are satisﬁed.”
Discussion: One-step sparse estimates in nonconcave penalized likelihood models (2008, methodology): “In [9] we introduced and studied the MC+, which offers fast, continuous, nearly unbiased and accurate penalized variable selection in high-dimensional linear regression, including the case of p n.”