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Debiasing the debiased Lasso with bootstrap (Q2192302)

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Language	Label	Description	Also known as
English	Debiasing the debiased Lasso with bootstrap	scientific article

Statements

scholarly article

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Debiasing the debiased Lasso with bootstrap (English)

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Electronic Journal of Statistics

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publication date

17 August 2020

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full work available at URL

https://arxiv.org/abs/1711.03613

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https://projecteuclid.org/euclid.ejs/1592359355

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zbMATH Keywords

confidence intervals

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high-dimensional models

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debiased Lasso

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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Inference on Treatment Effects after Selection among High-Dimensional Controls

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Square-root lasso: pivotal recovery of sparse signals via conic programming

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High-dimensional inference in misspecified linear models

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Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity

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The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).

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Asymptotic properties of the residual bootstrap for Lasso estimators

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Bootstrapping Lasso Estimators

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Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap

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Double/debiased machine learning for treatment and structural parameters

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

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High-dimensional simultaneous inference with the bootstrap

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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Testing and Confidence Intervals for High Dimensional Proportional Hazards Models

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Confidence intervals for high-dimensional inverse covariance estimation

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Confidence Intervals and Hypothesis Testing for High-Dimensional Regression

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Debiasing the Lasso: optimal sample size for Gaussian designs

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Asymptotics for Lasso-type estimators.

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Exact post-selection inference, with application to the Lasso

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A significance test for the lasso

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Bootstrap and wild bootstrap for high dimensional linear models

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High-dimensional graphs and variable selection with the Lasso

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Stability Selection

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Scaled sparse linear regression

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On asymptotically optimal confidence regions and tests for high-dimensional models

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Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)

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High-dimensional variable selection

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Nearly unbiased variable selection under minimax concave penalty

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Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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The Adaptive Lasso and Its Oracle Properties

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Identifiers

zbMATH Open document ID

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10.1214/20-EJS1713

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:2192302

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