Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates (Q6190704)

scientific article; zbMATH DE number 7813787

Language	Label	Description	Also known as
English	Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates	scientific article; zbMATH DE number 7813787

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instance of

scholarly article

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title

Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates (English)

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publication date

6 March 2024

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zbMATH Keywords

compositional data

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consistent estimation

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Huber loss

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Lasso

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support recovery

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Logistic-Normal Distributions: Some Properties and Uses

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Large Covariance Estimation for Compositional Data Via Composition-Adjusted Thresholding

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Multisample estimation of bacterial composition matrices in metagenomics data

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Robust inference via multiplier bootstrap

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Empirical properties of asset returns: stylized facts and statistical issues

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Adaptive Huber regression on Markov-dependent data

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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

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I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error

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Tuning Parameter Selection in High Dimensional Penalized Likelihood

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A general Bahadur representation of $M$-estimators and its application to linear regression with nonstochastic designs

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On parameters of increasing dimensions

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Robust Estimation of a Location Parameter

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Robust regression: Asymptotics, conjectures and Monte Carlo

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Variable selection in regression with compositional covariates

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

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Generalized linear models with linear constraints for microbiome compositional data

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High-dimensional graphs and variable selection with the Lasso

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Stability Selection

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Iteratively reweighted $\ell_1$-penalized robust regression

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The Generalized Lasso With Non-Linear Observations

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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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Error Distribution for Gene Expression Data

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Regression analysis for microbiome compositional data

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Adaptive Huber Regression

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Discriminant analysis for compositional data and robust parameter estimation

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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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A New Principle for Tuning-Free Huber Regression

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Asymptotic behavior of M-estimators for the linear model

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Nearly unbiased variable selection under minimax concave penalty

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

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Q3174050

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A new perspective on robust $M$-estimation: finite sample theory and applications to dependence-adjusted multiple testing

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published in

Journal of Business and Economic Statistics

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Identifiers

DOI

10.1080/07350015.2022.2097911

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6190704