Robust estimation of models for longitudinal data with dropouts and outliers-Reference-Cited by-同舟云学术

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Robust estimation of models for longitudinal data with dropouts and outliers

Published:2020-11-10 Issue: Volume: Page:1-24
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Zhang Yuexia¹,Qin Guoyou²^ORCID,Zhu Zhongyi³,Fu Bo⁴

Affiliation:

1. Department of Computer and Mathematical Sciences, University of Toronto, Toronto, Canada

2. Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, People's Republic of China

3. Department of Statistics, Fudan University, Shanghai, People's Republic of China

4. School of Data Science, Fudan University, Shanghai, People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2020.1845623

Reference39 articles.

1. Doubly Robust Estimation in Missing Data and Causal Inference Models

2. Multiply Robust Estimation in Regression Analysis With Missing Data

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multivariate contaminated normal mixture regression modeling of longitudinal data based on joint mean‐covariance model;Statistical Analysis and Data Mining: The ASA Data Science Journal;2023-12-22

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