Divide-and-conquer information-based optimal subdata selection algorithm

DOI10.1007/s42519-019-0048-5zbMath1425.62087arXiv1905.09948OpenAlexW3105524933WikidataQ127560775 ScholiaQ127560775MaRDI QIDQ2321778

Hai Ying Wang

Publication date: 23 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.09948

zbMATH Keywords

information matrixD-optimalitylinear regressionbig datainformation-based optimal subdata selection (IBOSS)subdata

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Statistical aspects of information-theoretic topics (62B10)

Related Items

Unnamed Item ⋮ Divide and conquer for accelerated failure time model with massive time‐to‐event data ⋮ Subdata selection based on orthogonal array for big data ⋮ Unnamed Item ⋮ Orthogonal subsampling for big data linear regression ⋮ Optimal subsampling for least absolute relative error estimators with massive data

Uses Software

• R
• Julia

Cites Work

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• Aggregated estimating equation estimation
• Distributed testing and estimation under sparse high dimensional models
• Optimal subsampling for softmax regression
• Julia: A Fresh Approach to Numerical Computing
• A split-and-conquer approach for analysis of
• On the relative stability of large order statistics
• Computational Limits of A Distributed Algorithm For Smoothing Spline
• Optimal Subsampling for Large Sample Logistic Regression
• An online updating approach for testing the proportional hazards assumption with streams of survival data
• Information-Based Optimal Subdata Selection for Big Data Linear Regression