Feature screening of quadratic inference functions for ultrahigh dimensional longitudinal data-Reference-Cited by-同舟云学术

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Feature screening of quadratic inference functions for ultrahigh dimensional longitudinal data

Published:2020-06-23 Issue:14 Volume:90 Page:2614-2630
ISSN:0094-9655
Container-title:Journal of Statistical Computation and Simulation
language:en
Short-container-title:Journal of Statistical Computation and Simulation

Author:

Lai Peng¹,Liang Weijuan²,Wang Fangjian¹,Zhang Qingzhao³⁴⁵

Affiliation:

1. School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, People's Republic of China

2. School of Statistics, Renmin University of China, Beijing, People's Republic of China

3. Department of Statistics, School of Economics, Xiamen University, Xiamen, People's Republic of China

4. Key Laboratory of Econometrics, Ministry of Education, Xiamen University, Xiamen, People's Republic of China

5. The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00949655.2020.1783666

Reference26 articles.

1. Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data

2. Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters

3. Improving generalised estimating equations using quadratic inference functions

4. Semiparametric Regression for Clustered Data Using Generalized Estimating Equations

5. New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis

Cited by 2 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. The effect of the working correlation on fitting models to longitudinal data;Scandinavian Journal of Statistics;2024-01-18

2. Feature screening of ultrahigh dimensional longitudinal data based on the C‐statistic;Statistical Analysis and Data Mining: The ASA Data Science Journal;2022-09-26

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