Distributed statistical optimization for non-randomly stored big data with application to penalized learning

DOI10.1007/s11222-023-10247-xzbMath1516.62030MaRDI QIDQ6172933

Kang-Ning Wang, Shaomin Li

Publication date: 20 July 2023

Published in: Statistics and Computing (Search for Journal in Brave)

zbMATH Keywords

high dimensionalitybig datadistributed optimizationpenalized learning

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) Statistical aspects of big data and data science (62R07)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• A partially linear framework for massive heterogeneous data
• Aggregated estimating equation estimation
• One-step sparse estimates in nonconcave penalized likelihood models
• Distributed testing and estimation under sparse high dimensional models
• A distributed one-step estimator
• Nonconcave penalized likelihood with a diverging number of parameters.
• Robust distributed modal regression for massive data
• Distributed estimation of principal eigenspaces
• Quantile regression under memory constraint
• Quantile regression in big data: a divide and conquer based strategy
• Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
• A split-and-conquer approach for analysis of
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Communication-Efficient Distributed Statistical Inference
• Model Selection and Estimation in Regression with Grouped Variables
• Communication-Efficient Accurate Statistical Estimation

This page was built for publication: Distributed statistical optimization for non-randomly stored big data with application to penalized learning