Lasso, fractional norm and structured sparse estimation using a Hadamard product parametrization

DOI10.1016/j.csda.2017.06.007zbMath1466.62098arXiv1611.00040OpenAlexW2546982352WikidataQ114671397 ScholiaQ114671397MaRDI QIDQ1658387

Peter D. Hoff

Publication date: 14 August 2018

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

Full work available at URL: https://arxiv.org/abs/1611.00040

zbMATH Keywords

optimizationridge regressionlinear regressionspatial autocorrelationsparsitygeneralized linear modelcyclic coordinate descent

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (3)

Smooth over-parameterized solvers for non-smooth structured optimization ⋮ Neuronized Priors for Bayesian Sparse Linear Regression ⋮ Understanding Implicit Regularization in Over-Parameterized Single Index Model

Uses Software

• penalized
• glmnet

Cites Work

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• Linear and nonlinear programming.
• One-step sparse estimates in nonconcave penalized likelihood models
• The Lasso problem and uniqueness
• Hadamard products and multivariate statistical analysis
• Variable selection using MM algorithms
• Inference with normal-gamma prior distributions in regression problems
• The Bayesian Lasso
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Model Selection and Estimation in Regression with Grouped Variables
• On a Factorisation of Positive Definite Matrices

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