A Tuning-free Robust and Efficient Approach to High-dimensional Regression (Q5146020)

scientific article; zbMATH DE number 7300149

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English	A Tuning-free Robust and Efficient Approach to High-dimensional Regression	scientific article; zbMATH DE number 7300149

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instance of

scholarly article

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title

A Tuning-free Robust and Efficient Approach to High-dimensional Regression (English)

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Journal of the American Statistical Association

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publication date

22 January 2021

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zbMATH Keywords

efficiency

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heavy-tailed error

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high dimension

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linear regression

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tuning parameter

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robustness

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describes a project that uses

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MaRDI publication profile

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full work available at URL

https://doi.org/10.1080/01621459.2020.1840989

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cites work

Robust and consistent variable selection in high-dimensional generalized linear models

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

0 references

Simultaneous analysis of Lasso and Dantzig selector

0 references

Prediction error bounds for linear regression with the TREX

0 references

Q4821526

0 references

Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection

0 references

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

0 references

Sparsity oracle inequalities for the Lasso

0 references

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

0 references

Extended Bayesian information criteria for model selection with large model spaces

0 references

Atomic Decomposition by Basis Pursuit

0 references

A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees

0 references

Q2810890

0 references

Ranking and empirical minimization of $U$-statistics

0 references

Variance estimation in high-dimensional linear models

0 references

Adaptive robust variable selection

0 references

Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression

0 references

Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions

0 references

Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

0 references

A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)

0 references

Tuning Parameter Selection in High Dimensional Penalized Likelihood

0 references

Rank-based inference for the single-index model

0 references

The restricted consistency property of leave-nv-out cross-validation for high-dimensional variable selection

0 references

How Correlations Influence Lasso Prediction

0 references

Q4365260

0 references

Risk consistency of cross-validation with Lasso-type procedures

0 references

Estimating Regression Coefficients by Minimizing the Dispersion of the Residuals

0 references

Q3413299

0 references

Probability in Banach spaces. Isoperimetry and processes

0 references

Model Selection via Bayesian Information Criterion for Quantile Regression Models

0 references

Q3405561

0 references

Statistical consistency and asymptotic normality for high-dimensional robust $M$-estimators

0 references

Minimum distance Lasso for robust high-dimensional regression

0 references

High-dimensional graphs and variable selection with the Lasso

0 references

Rank regression with estimated scores

0 references

A resampling method based on pivotal estimating functions

0 references

Robust Estimation via Robust Gradient Estimation

0 references

A permutation approach for selecting the penalty parameter in penalized model selection

0 references

Adaptive Huber Regression

0 references

Scaled sparse linear regression

0 references

Sparse Matrix Inversion with Scaled Lasso

0 references

Q4864293

0 references

High-dimensional generalized linear models and the lasso

0 references

Weak convergence and empirical processes. With applications to statistics

0 references

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

0 references

Shrinkage Tuning Parameter Selection with a Diverging number of Parameters

0 references

Tuning parameter selectors for the smoothly clipped absolute deviation method

0 references

The $L_1$ penalized LAD estimator for high dimensional linear regression

0 references

Local Rank Inference for Varying Coefficient Models

0 references

Calibrating nonconvex penalized regression in ultra-high dimension

0 references

Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method

0 references

Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension

0 references

Robust Variable Selection With Exponential Squared Loss

0 references

Estimating the error variance in a high-dimensional linear model

0 references

Nearly unbiased variable selection under minimax concave penalty

0 references

The sparsity and bias of the LASSO selection in high-dimensional linear regression

0 references

A general theory of concave regularization for high-dimensional sparse estimation problems

0 references

Q2896066

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Q3174050

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Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models

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Identifiers

zbMATH Open document ID

1452.62525

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DOI

10.1080/01621459.2020.1840989

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:5146020