Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data

DOI10.1080/01621459.2020.1773832zbMath1506.62235arXiv2005.10435OpenAlexW3028903392MaRDI QIDQ5881082

Hai Ying Wang, Jun Yu, Ming-Yao Ai, Hui-Ming Zhang

Publication date: 9 March 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.10435

zbMATH Keywords

quasi-likelihoodbig dataPoisson samplingdistributed subsampling

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Sampling theory, sample surveys (62D05) Statistical aspects of big data and data science (62R07) Causal inference from observational studies (62D20)

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Uses Software

• R
• Fahrmeir

Cites Work

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• Aggregated estimating equation estimation
• A note on the uniqueness of the quasi-likelihood estimator
• Faster least squares approximation
• Model assisted survey sampling
• Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs
• Local polynomial regresssion estimators in survey sampling.
• Asymptotic Statistics
• Optimal Subsampling for Large Sample Logistic Regression
• A Scalable Bootstrap for Massive Data
• Information-Based Optimal Subdata Selection for Big Data Linear Regression
• Communication-Efficient Distributed Statistical Inference
• Speeding Up MCMC by Efficient Data Subsampling
• Optimal Design of Experiments
• Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
• Empirical Likelihood Confidence Intervals for Complex Sampling Designs
• Multivariate statistical modelling based on generalized linear models.

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