Bootstrapping Lasso-type estimators in regression models

DOI10.1016/j.jspi.2018.05.007zbMath1418.62185OpenAlexW2808538518WikidataQ129699048 ScholiaQ129699048MaRDI QIDQ2317244

Mihai C. Giurcanu, Presnell, Brett

Publication date: 9 August 2019

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2018.05.007

zbMATH Keywords

regression modelsoracle propertybootstrap methodsrandom predictorsLasso-type estimators

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09)

Related Items

Thresholding tests based on affine Lasso to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension, Bootstrap inference for a class of non-regular estimators

Uses Software

• R
• emplik
• glmnet

Cites Work

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• Large Sample Properties of Generalized Method of Moments Estimators
• The Adaptive Lasso and Its Oracle Properties
• Thresholding least-squares inference in high-dimensional regression models
• Bootstrapping in non-regular smooth function models
• Empirical likelihood ratio confidence regions
• On the distribution of penalized maximum likelihood estimators: the LASSO, SCAD, and thresholding
• Some asymptotic theory for the bootstrap
• Bootstrapping regression models
• Bootstrap methods: another look at the jackknife
• Resampling: consistency of substitution estimators
• Diagnosing bootstrap success
• Bootstrap inference for misspecified moment condition models
• Asymptotics for Lasso-type estimators.
• Bootstrap and wild bootstrap for high dimensional linear models
• Penalized regression, standard errors, and Bayesian Lassos
• A note on methods of restoring consistency to the bootstrap
• Bootstrapping Lasso Estimators
• Intentionally Biased Bootstrap Methods
• Variational Analysis
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• A Statistical View of Some Chemometrics Regression Tools
• Real Analysis and Probability
• Regularization and Variable Selection Via the Elastic Net