Ridge regression revisited: debiasing, thresholding and bootstrap (Q2148980)

scientific article; zbMATH DE number 7547935

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English	Ridge regression revisited: debiasing, thresholding and bootstrap	scientific article; zbMATH DE number 7547935

Statements

instance of

scholarly article

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title

Ridge regression revisited: debiasing, thresholding and bootstrap (English)

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The Annals of Statistics

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publication date

24 June 2022

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full work available at URL

https://arxiv.org/abs/2009.08071

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zbMATH Keywords

Gaussian approximation

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high-dimensional data

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prediction

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regression

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resampling

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describes a project that uses

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Limit of the smallest eigenvalue of a large dimensional sample covariance matrix

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Regularized estimation in sparse high-dimensional time series models

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Simultaneous analysis of Lasso and Dantzig selector

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Statistical significance in high-dimensional linear models

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Statistics for high-dimensional data. Methods, theory and applications.

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Asymptotic properties of the residual bootstrap for Lasso estimators

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Bootstrapping Lasso Estimators

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Robust inference via multiplier bootstrap

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Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors

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Broken adaptive ridge regression and its asymptotic properties

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On the prediction performance of the Lasso

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High-dimensional simultaneous inference with the bootstrap

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High-dimensional asymptotics of prediction: ridge regression and classification

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Making wald tests work for cointegrated VAR systems

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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BLOCK BOOTSTRAP HAC ROBUST TESTS: THE SOPHISTICATION OF THE NAIVE BOOTSTRAP

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Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization

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Q4902474

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False discovery rate control via debiased Lasso

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Debiasing the Lasso: optimal sample size for Gaussian designs

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Exact post-selection inference, with application to the Lasso

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Distribution-free Prediction Bands for Non-parametric Regression

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Bootstrap procedures under some non-i.i.d. models

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Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression

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Bootstrap and wild bootstrap for high dimensional linear models

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Empirical process of residuals for high-dimensional linear models

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High-dimensional graphs and variable selection with the Lasso

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Lasso-type recovery of sparse representations for high-dimensional data

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Model-free prediction and regression. A transformation-based approach to inference

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Subsampling

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Deep Knockoffs

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Mathematical Statistics

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Estimation in high-dimensional linear models with deterministic design matrices

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Bootstrap Prediction Intervals for Regression

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Robust trend inference with series variance estimator and testing-optimal smoothing parameter

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A heteroskedasticity and autocorrelation robust<i>F</i>test using an orthonormal series variance estimator

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Scaled sparse linear regression

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Q4864293

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Uniform asymptotic inference and the bootstrap after model selection

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High-dimensional generalized linear models and the lasso

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On the asymptotic variance of the debiased Lasso

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The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)

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Jackknife, bootstrap and other resampling methods in regression analysis

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Gaussian approximation for high dimensional time series

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Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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Q3174050

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Identifiers

DOI

10.1214/21-AOS2156

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:2148980