scientific article; zbMATH DE number 7306878
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Publication:5148971
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Publication date: 5 February 2021
Full work available at URL: https://arxiv.org/abs/1906.05741
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Related Items (16)
A review of distributed statistical inference ⋮ Distributed estimation in heterogeneous reduced rank regression: with application to order determination in sufficient dimension reduction ⋮ Robust reduced rank regression in a distributed setting ⋮ Single-index composite quantile regression for ultra-high-dimensional data ⋮ Byzantine-robust distributed sparse learning for \(M\)-estimation ⋮ Optimal subsampling for large‐sample quantile regression with massive data ⋮ Distributed Sparse Composite Quantile Regression in Ultrahigh Dimensions ⋮ Distributed Estimation for Principal Component Analysis: An Enlarged Eigenspace Analysis ⋮ First-Order Newton-Type Estimator for Distributed Estimation and Inference ⋮ Unnamed Item ⋮ Distributed Decoding From Heterogeneous 1-Bit Compressive Measurements ⋮ Communication-efficient surrogate quantile regression for non-randomly distributed system ⋮ Communication-efficient distributed estimation for high-dimensional large-scale linear regression ⋮ Distributed statistical optimization for non-randomly stored big data with application to penalized learning ⋮ An Asymptotic Analysis of Random Partition Based Minibatch Momentum Methods for Linear Regression Models ⋮ Proximal nested primal-dual gradient algorithms for distributed constraint-coupled composite optimization
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Cites Work
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- On asymptotically optimal confidence regions and tests for high-dimensional models
- Nearly unbiased variable selection under minimax concave penalty
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- A partially linear framework for massive heterogeneous data
- Statistics for high-dimensional data. Methods, theory and applications.
- Distributed testing and estimation under sparse high dimensional models
- Divide and conquer in nonstandard problems and the super-efficiency phenomenon
- A new perspective on robust \(M\)-estimation: finite sample theory and applications to dependence-adjusted multiple testing
- Robust covariance and scatter matrix estimation under Huber's contamination model
- Least squares after model selection in high-dimensional sparse models
- Learning from MOM's principles: Le Cam's approach
- Robust machine learning by median-of-means: theory and practice
- Distributed estimation of principal eigenspaces
- Quantile regression under memory constraint
- Risk minimization by median-of-means tournaments
- Regularization, sparse recovery, and median-of-means tournaments
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Adaptive robust variable selection
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- A Massive Data Framework for M-Estimators with Cubic-Rate
- Adaptive Huber Regression
- An algorithm for quadratic ℓ1-regularized optimization with a flexible active-set strategy
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Bootstrap Methods for Median Regression Models
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- Communication-Efficient Distributed Statistical Inference
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
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