Robust distributed estimation and variable selection for massive datasets via rank regression
From MaRDI portal
Publication:2135513
DOI10.1007/S10463-021-00803-5OpenAlexW3173784385MaRDI QIDQ2135513
Publication date: 9 May 2022
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10463-021-00803-5
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The Adaptive Lasso and Its Oracle Properties
- Aggregated estimating equation estimation
- Distributed testing and estimation under sparse high dimensional models
- Robust spline-based variable selection in varying coefficient model
- Distributed estimation of principal eigenspaces
- Quantile regression in big data: a divide and conquer based strategy
- Robust analysis of linear models
- Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
- LOCAL RANK ESTIMATION OF TRANSFORMATION MODELS WITH FUNCTIONAL COEFFICIENTS
- Local Rank Inference for Varying Coefficient Models
- A split-and-conquer approach for analysis of
- Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
- Regression Quantiles
- On the optimality of averaging in distributed statistical learning
- Communication-Efficient Distributed Statistical Inference
This page was built for publication: Robust distributed estimation and variable selection for massive datasets via rank regression