Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data-Reference-Cited by-同舟云学术

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

Published:2020-07-07 Issue: Volume: Page:1-12
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Yu Jun¹,Wang HaiYing²,Ai Mingyao³,Zhang Huiming⁴

Affiliation:

1. School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China;

2. Department of Statistics, University of Connecticut, Storrs, CT;

3. LMAM, School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China;

4. School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1773832

Reference28 articles.

1. Empirical likelihood confidence intervals for complex sampling designs

2. Local polynomial regresssion estimators in survey sampling

3. Strong consistency of maximum quasi-likelihood estimators in generalized linear models with fixed and adaptive designs

4. Dhillon, P. S., Lu, Y., Foster, D., and Ungar, L. (2013), “New Subsampling Algorithms for Fast Least Squares Regression,” in International Conference on Neural Information Processing Systems, pp. 360–368.

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