Support recovery without incoherence: a case for nonconvex regularization
From MaRDI portal
Publication:682289
DOI10.1214/16-AOS1530zbMath1385.62008arXiv1412.5632OpenAlexW2964346891MaRDI QIDQ682289
Po-Ling Loh, Martin J. Wainwright
Publication date: 14 February 2018
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1412.5632
Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07)
Related Items
Bayesian Estimation of Gaussian Conditional Random Fields, Bias versus non-convexity in compressed sensing, On the sign consistency of the Lasso for the high-dimensional Cox model, Second-order optimality conditions and improved convergence results for regularization methods for cardinality-constrained optimization problems, Almost sure uniqueness of a global minimum without convexity, Structure learning of sparse directed acyclic graphs incorporating the scale-free property, Difference-of-Convex Learning: Directional Stationarity, Optimality, and Sparsity, On high-dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization, Byzantine-robust distributed sparse learning for \(M\)-estimation, Penalized wavelet nonparametric univariate logistic regression for irregular spaced data, An ensemble EM algorithm for Bayesian variable selection, Unnamed Item, High‐dimensional sparse multivariate stochastic volatility models, Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data, Robust High-Dimensional Regression with Coefficient Thresholding and Its Application to Imaging Data Analysis, Statistical analysis of sparse approximate factor models, Which bridge estimator is the best for variable selection?, An unbiased approach to compressed sensing, Finite-sample analysis of \(M\)-estimators using self-concordance, Sparse regression: scalable algorithms and empirical performance, A discussion on practical considerations with sparse regression methodologies, Oracle Inequalities for Local and Global Empirical Risk Minimizers, I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error, Consistency bounds and support recovery of d-stationary solutions of sparse sample average approximations, Inference in high dimensional linear measurement error models, Iteratively reweighted \(\ell_1\)-penalized robust regression, Sparse classification: a scalable discrete optimization perspective, The finite sample properties of sparse M-estimators with pseudo-observations, Bayesian Regularization for Graphical Models With Unequal Shrinkage, High-dimensional robust approximated \(M\)-estimators for mean regression with asymmetric data, Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO, High dimensional generalized linear models for temporal dependent data
