Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts
Author:
Publisher
Springer Science and Business Media LLC
Subject
Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s10463-020-00761-4.pdf
Reference41 articles.
1. Ai, C., Linton, O., Zhang, Z. (2018). A simple and efficient estimation method for models with nonignorable missing data. Statistica Sinica, to appear.
2. Bai, Y., Fung, W. K., Zhu, Z. (2010). Weighted empirical likelihood for generalized linear models with longitudinal data. Journal of Statistical Planning and Inference, 140, 3446–3456.
3. Cantoni, E., Flemming, J. M., Ronchetti, E. (2005). Variable selection for marginal longitudinal generalized linear models. Biometrics, 61, 507–514.
4. Chen, J., Chen, Z. (2008). Extended Bayesian information criterion for model selection with large sample space. Biometrika, 95, 759–771.
5. Cho, H., Qu, A. (2015). Efficient estimation for longitudinal data by combining large-dimensional moment conditions. Electronic Journal of Statistics, 9, 1315–1334.
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