Scalable inference for high-dimensional precision matrix

DOI10.1080/03610926.2021.1890778OpenAlexW3130087350MaRDI QIDQ5046808

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Publication date: 9 November 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2021.1890778

zbMATH Keywords

scalabilitystatistical inferencehigh dimensionalitybias correctiongraphical constrained projection

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• On asymptotically optimal confidence regions and tests for high-dimensional models
• Confidence intervals for high-dimensional inverse covariance estimation
• Asymptotic normality and optimalities in estimation of large Gaussian graphical models
• Honest confidence regions and optimality in high-dimensional precision matrix estimation
• Innovated scalable efficient estimation in ultra-large Gaussian graphical models
• Variable selection after screening: with or without data splitting?
• Combinatorial inference for graphical models
• In defense of the indefensible: a very naïve approach to high-dimensional inference
• Fast and adaptive sparse precision matrix estimation in high dimensions
• High-dimensional variable screening and bias in subsequent inference, with an empirical comparison
• Innovated interaction screening for high-dimensional nonlinear classification
• Optimal classification in sparse Gaussian graphic model
• High-dimensional graphs and variable selection with the Lasso
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs
• Model selection and estimation in the Gaussian graphical model
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• A Flexible Framework for Hypothesis Testing in High Dimensions
• High Dimensional Ordinary Least Squares Projection for Screening Variables
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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