On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property (Q494167)

scientific article; zbMATH DE number 6477030

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English	On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property	scientific article; zbMATH DE number 6477030

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instance of

scholarly article

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title

On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property (English)

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published in

Journal of Econometrics

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publication date

31 August 2015

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zbMATH Keywords

asymptotic uniform linearity

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Brownian bridge

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oracle property

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prediction intervals

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regularization

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weak convergence

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full work available at URL

https://doi.org/10.1016/j.jeconom.2015.02.012

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cites work

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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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Strong consistency of Lasso estimators

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Bootstrapping Lasso Estimators

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Weak convergence of the empirical process of residuals in linear models with many parameters

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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Probability Inequalities for Sums of Independent Random Variables

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A note on the asymptotic distribution of lasso estimator for correlated data

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Asymptotic properties of bridge estimators in sparse high-dimensional regression models

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Q3543468

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Asymptotics for Lasso-type estimators.

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Asymptotic Behavior of Wilcoxon Type Confidence Regions in Multiple Linear Regression

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Weighted empirical processes in dynamic nonlinear models.

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Sparse estimators and the oracle property, or the return of Hodges' estimator

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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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Asymptotic behavior of M-estimators of p regression parameters when $p^ 2/n$ is large. I. Consistency

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Asymptotic behavior of M estimators of p regression parameters when $p^ 2/n$ is large. II: Normal approximation

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Asymptotic behavior of the empiric distribution of M-estimated residuals from a regression model with many parameters

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On the distribution of the adaptive LASSO estimator

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Q4864293

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Weak convergence and empirical processes. With applications to statistics

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

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Model selection and estimation in the Gaussian graphical model

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Nearly unbiased variable selection under minimax concave penalty

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

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Q3174050

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The Adaptive Lasso and Its Oracle Properties

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Identifiers

zbMATH Open document ID

1331.62345

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Mathematics Subject Classification ID

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10.1016/J.JECONOM.2015.02.012

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Sitelinks

Mathematics(1 entry)

mardi Publication:494167