Efficient fused learning for distributed imbalanced data

DOI10.1080/03610926.2020.1759641OpenAlexW3025068297MaRDI QIDQ5079858

No author found.

Publication date: 30 May 2022

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

Full work available at URL: https://doi.org/10.1080/03610926.2020.1759641

zbMATH Keywords

logistic regressioncase-control studiesoracle estimatordistributed imbalanced data

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A partially linear framework for massive heterogeneous data
• Distributed testing and estimation under sparse high dimensional models
• Biased sampling, over-identified parameter problems and beyond
• Distributed inference for quantile regression processes
• On the existence of maximum likelihood estimates in logistic regression models
• A case-cohort design for epidemiologic cohort studies and disease prevention trials
• Logistic disease incidence models and case-control studies
• Fitting Logistic Regression Models in Stratified Case-Control Studies
• Infinite Parameter Estimates in Logistic Regression, with Application to Approximate Conditional Inference
• Regression Analysis with Response-selective Sampling
• Case-cohort and case-control analysis with Cox's model
• Efficient Estimation of Semiparametric Transformation Models for Two-Phase Cohort Studies

This page was built for publication: Efficient fused learning for distributed imbalanced data