Robust analysis of longitudinal data with nonignorable missing responses
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References listed on IDEAS
- Dantan Etienne & Proust-Lima Cécile & Letenneur Luc & Jacqmin-Gadda Helene, 2008. "Pattern Mixture Models and Latent Class Models for the Analysis of Multivariate Longitudinal Data with Informative Dropouts," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, July.
- Geert Verbeke & Geert Molenberghs, 2005. "Longitudinal and incomplete clinical studies," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 143-176.
- J. G. Ibrahim & S. R. Lipsitz & M.-H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non-ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
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- Yu-Ye Zou & Han-Ying Liang & Jing-Jing Zhang, 2015. "Nonlinear wavelet density estimation with data missing at random when covariates are present," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 967-995, November.
- Xia, Ye-Mao & Tang, Nian-Sheng & Gou, Jian-Wei, 2016. "Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 259-275.
- Guoyou Qin & Zhongyi Zhu & Wing K. Fung, 2016. "Robust estimation of generalized partially linear model for longitudinal data with dropouts," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(5), pages 977-1000, October.
More about this item
KeywordsGeneralized linear models; Incomplete data; Missing responses; Mixed models; Robust estimation;
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