Efficient Empirical Bayes Variable Selection and Estimation in Linear Models

By Ming Yuan, Yi Lin. 2005

223 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/808d76783c14a12680031cd48b9220741d56559e

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Citations in the corpus

On the non‐negative garrotte estimator (2007, methodology): “Various other variable selection methods have been introduced in recent years (George and McCulloch, 1993; Foster and George, 1994; Breiman, 1995; Tibshirani, 1996; George and Foster, 2000; Fan and Li, 2001; Shen and Ye, 2002; Efron et al., 2004; Yuan and Lin, 2005; Zou and Hastie, 2005).”
The Bayesian Lasso (2008, result): “Motivated by this connection, several other authors subsequently proposed using Laplace-like priors (e.g., Figueiredo 2003; Bae and Mallick 2004; Yuan and Lin 2005).”
GENERALIZED DOUBLE PARETO SHRINKAGE. (2011, methodology): “There is a rich literature analyzing the Lasso and related approaches (Fu, 1998; Knight and Fu, 2000; Fan and Li, 2001; Yuan and Lin, 2005; Zhao and Yu, 2006; Zou, 2006; Zou and Li, 2008), with a number of articles considering asymptotic properties.”
One-step Sparse Estimates in Nonconcave Penalized Likelihood Models (2008, unknown): “The LARS algorithm was used to simplify the computations in an empirical Bayes model for LASSO (Yuan and Lin [34]).”
Compatibility of prior specifications across linear models (2008, methodology): “…hierarchical mixture priors as in George and McCulloch (1997), or using an empirical Bayes approach, as in George and Forster (2000), and more recently in Yuan and Lin (2005), or employing a blend of noninformative and conjugate procedures, as exemplified in Fernández, Ley and Steel (2001).”
Needles and Straw in a Haystack: Posterior concentration for possibly sparse sequences (2012, background): “Other related papers include [19], [6], [7], [14], [15], [5].”
BAYESIAN LINEAR REGRESSION WITH SPARSE PRIORS (2014, background): “2) were considered by many authors, including [8, 20, 21, 26, 34, 41, 48].”
The Horseshoe Estimator: Posterior Concentration around Nearly Black Vectors (2014, unknown): “…the minimax rate in ℓ q norm, q ∈ (0 , 2], for mean vectors that are nearly black, and in ℓ q norm, q ∈ (0 , 2) for mean vectors of bounded weak ℓ p norm, p ∈ (0 , q ). includes Other work on empirical Bayes approaches to the two-group model (Efron, 2008; Jiang and Zhang, 2009; Yuan and Lin, 2005).”