Relaxed Lasso

DOI10.1016/j.csda.2006.12.019zbMath1452.62522OpenAlexW4229873072WikidataQ105583591 ScholiaQ105583591MaRDI QIDQ1020826

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.12.019

zbMATH Keywords

high dimensionality dimensionality reduction Lasso \(\ell_q\)-norm penalisation bridge estimation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

Multiscale hierarchical decomposition methods for ill-posed problems, Bootstrapping some GLM and survival regression variable selection estimators, Tuning parameter selection for penalized estimation via \(R^2\), Topological techniques in model selection, Distributed online multi‐task sparse identification for multiple systems with asynchronous updates, An ensemble EM algorithm for Bayesian variable selection, Multi-task sparse identification for closed-loop systems with general observation sequences, Cross-Fitted Residual Regression for High-Dimensional Heteroscedasticity Pursuit, On the impact of model selection on predictor identification and parameter inference, Markov Neighborhood Regression for High-Dimensional Inference, An extended variable inclusion and shrinkage algorithm for correlated variables, Interquantile shrinkage and variable selection in quantile regression, Stabilizing the Lasso against cross-validation variability, Penalized and constrained LAD estimation in fixed and high dimension, Monotone splines Lasso, Variable selection for semiparametric regression models with iterated penalisation, Some sharp performance bounds for least squares regression with \(L_1\) regularization, High-dimensional variable selection, Variable Selection With Second-Generation P-Values, On the robustness of the generalized fused Lasso to prior specifications, Bayesian reciprocal LASSO quantile regression, Joint estimation and variable selection for mean and dispersion in proper dispersion models, Clustering, multicollinearity, and singular vectors, A rank-corrected procedure for matrix completion with fixed basis coefficients, Robust error density estimation in ultrahigh dimensional sparse linear model, A component lasso, High-dimensional regression with potential prior information on variable importance, MIP-BOOST: Efficient and Effective L₀ Feature Selection for Linear Regression, Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies, Degrees of freedom for piecewise Lipschitz estimators, A SAEM algorithm for fused Lasso penalized nonlinear mixed effect models: application to group comparison in pharmacokinetics, A novel elastic net-based NGBMC(1,n) model with multi-objective optimization for nonlinear time series forecasting, Sparse regression with multi-type regularized feature modeling, Ensemble of penalized logistic models for classification of high-dimensional data, Exploiting Disagreement Between High-Dimensional Variable Selectors for Uncertainty Visualization, Statistical significance in high-dimensional linear models, Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression, Correlated variables in regression: clustering and sparse estimation, The pre-image problem for Laplacian Eigenmaps utilizing L ₁ regularization with applications to data fusion, Feature selection in machine learning: an exact penalty approach using a difference of convex function algorithm, Sample average approximation with heavier tails II: localization in stochastic convex optimization and persistence results for the Lasso, D-trace estimation of a precision matrix using adaptive lasso penalties, Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown, Projective inference in high-dimensional problems: prediction and feature selection, A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates, Variables selection using \(\mathcal{L}_0\) penalty, High-dimensional generalized linear models and the lasso, Prediction error after model search, A two-step method for estimating high-dimensional Gaussian graphical models, SLOPE-adaptive variable selection via convex optimization, Semiparametric regression models with additive nonparametric components and high dimensional parametric components, A majorization-minimization approach to variable selection using spike and slab priors, Sparse least trimmed squares regression for analyzing high-dimensional large data sets, Thresholding-based iterative selection procedures for model selection and shrinkage, The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods, Sparse estimation technique for digital pre-distortion of impedance-mismatched power amplifiers, Sign-constrained least squares estimation for high-dimensional regression, Unnamed Item, Variable selection approach for zero-inflated count data via adaptive lasso, RandGA: injecting randomness into parallel genetic algorithm for variable selection, Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine, Model selection strategies for identifying most relevant covariates in homoscedastic linear models, Stochastic correlation coefficient ensembles for variable selection, Empirical likelihood test for high dimensional linear models, Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons, A discussion on practical considerations with sparse regression methodologies, Discussion of ``Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons, A look at robustness and stability of \(\ell_1\)-versus \(\ell_0\)-regularization: discussion of papers by Bertsimas et al. and Hastie et al., Stochastic identification of malware with dynamic traces, Discussion: One-step sparse estimates in nonconcave penalized likelihood models, Beyond support in two-stage variable selection, Empirical comparison study of approximate methods for structure selection in binary graphical models, Nonconcave penalized composite conditional likelihood estimation of sparse Ising models, Improved variable selection with forward-lasso adaptive shrinkage, Random lasso, Sparse modeling of categorial explanatory variables, A Bayesian lasso via reversible-jump MCMC, Least angle and \(\ell _{1}\) penalized regression: a review, Nonparametric Statistics and High/Infinite Dimensional Data, Nearly unbiased variable selection under minimax concave penalty, Sparse kernel learning with LASSO and Bayesian inference algorithm, Sparse regulatory networks, Network classification with applications to brain connectomics, On the sensitivity of the Lasso to the number of predictor variables, High-dimensional classification using features annealed independence rules, Lasso-type recovery of sparse representations for high-dimensional data, Some theoretical results on the grouped variables Lasso, Detection of gene-gene interactions using multistage sparse and low-rank regression, Optimal EMG placement for a robotic prosthesis controller with sequential, adaptive functional estimation (SAFE), Component-wisely sparse boosting, Two tales of variable selection for high dimensional regression: Screening and model building, Editorial: Statistical learning methods including dimensionality reduction, SLASSO: a scaled LASSO for multicollinear situations, Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals, Bootstrapping multiple linear regression after variable selection, Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case, Smoothly adaptively centered ridge estimator, On Lasso refitting strategies, High-dimensional additive modeling, Unnamed Item, Unnamed Item, Penalized robust estimators in sparse logistic regression, Sure Independence Screening for Ultrahigh Dimensional Feature Space, Lasso and probabilistic inequalities for multivariate point processes, High dimensional single index models, A group VISA algorithm for variable selection, On the Effect and Remedies of Shrinkage on Classification Probability Estimation

Cites Work