[HTML][HTML] A selective review of group selection in high-dimensional models
Grou** structures arise naturally in many statistical modeling problems. Several methods
have been proposed for variable selection that respect grou** structure in variables …
have been proposed for variable selection that respect grou** structure in variables …
Decoupling shrinkage and selection in Bayesian linear models: a posterior summary perspective
Selecting a subset of variables for linear models remains an active area of research. This
article reviews many of the recent contributions to the Bayesian model selection and …
article reviews many of the recent contributions to the Bayesian model selection and …
Statistical learning with sparsity
In this monograph, we have attempted to summarize the actively develo** field of
statistical learning with sparsity. A sparse statistical model is one having only a small …
statistical learning with sparsity. A sparse statistical model is one having only a small …
Best subset, forward stepwise or lasso? Analysis and recommendations based on extensive comparisons
In exciting recent work, Bertsimas, King and Mazumder (Ann. Statist. 44 (2016) 813–852)
showed that the classical best subset selection problem in regression modeling can be …
showed that the classical best subset selection problem in regression modeling can be …
Best subset selection via a modern optimization lens
D Bertsimas, A King, R Mazumder - 2016 - projecteuclid.org
Best subset selection via a modern optimization lens Page 1 The Annals of Statistics 2016, Vol.
44, No. 2, 813–852 DOI: 10.1214/15-AOS1388 © Institute of Mathematical Statistics, 2016 …
44, No. 2, 813–852 DOI: 10.1214/15-AOS1388 © Institute of Mathematical Statistics, 2016 …
Coordinate descent algorithms
SJ Wright - Mathematical programming, 2015 - Springer
Coordinate descent algorithms solve optimization problems by successively performing
approximate minimization along coordinate directions or coordinate hyperplanes. They have …
approximate minimization along coordinate directions or coordinate hyperplanes. They have …
Extended comparisons of best subset selection, forward stepwise selection, and the lasso
In exciting new work, Bertsimas et al.(2016) showed that the classical best subset selection
problem in regression modeling can be formulated as a mixed integer optimization (MIO) …
problem in regression modeling can be formulated as a mixed integer optimization (MIO) …
The spike-and-slab lasso
Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection,
its potential for penalized likelihood estimation has largely been overlooked. In this article …
its potential for penalized likelihood estimation has largely been overlooked. In this article …
[LIBRO][B] MM optimization algorithms
K Lange - 2016 - SIAM
Algorithms have never been more important. As the recipes of computer programs,
algorithms rule our lives. Although they can be forces for both good and evil, this is not a …
algorithms rule our lives. Although they can be forces for both good and evil, this is not a …
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
We provide novel theoretical results regarding local optima of regularized M-estimators,
allowing for nonconvexity in both loss and penalty functions. Under restricted strong …
allowing for nonconvexity in both loss and penalty functions. Under restricted strong …