LongitudinalDA 论文列表

论文列表

Alencar, A. P., Singer, J. M., and Rocha, F. M. M. (2012). Competing regression models for longitudinal data.

Biometrical Journal, 54(2), 214-229. [pdf]
Proust‐Lima, C., Letenneur, L., and Jacqmin‐Gadda, H. (2007). A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome.

Statistics in Medicine, 26(10), 2229-2245. [pdf]
Rhee, A., Kwak, M. S., and Lee, K. (2022). Robust modeling of multivariate longitudinal data using modified Cholesky and hypersphere decompositions.

Computational Statistics and Data Analysis, 170, 107439. [pdf]
Xu, L., Xiang, S., and Yao, W. (2019). Robust maximum $L_q$-likelihood estimation of joint mean–covariance models for longitudinal data.

Journal of Multivariate Analysis, 171, 397-411. [pdf]

Buscemi, S., and Plaia, A. (2020). Model selection in linear mixed-effect models.

AStA Advances in Statistical Analysis, 104(4), 529-575. [pdf]
Huang, Y., and Lu, T. (2017). Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features.

Computational Statistics, 32(1), 179-196. [pdf]
Kock, L., Klein, N., and Nott, D. J. (2023). Deep mixture of linear mixed models for complex longitudinal data.

arXiv preprint arXiv:2311.07156. [pdf]
Maestrini, L., Hui, F. K., and Welsh, A. H. (2024). Restricted maximum likelihood estimation in generalized linear mixed models.

arXiv preprint arXiv:2402.12719. [pdf]
Saraceno, G., Ghosh, A., Basu, A., and Agostinelli, C. (2024). Robust estimation of fixed effect parameters and variances of linear mixed models: the minimum density power divergence approach.

AStA Advances in Statistical Analysis, 108(1), 127-157. [pdf] [supplement]
Taavoni, M., Arashi, M., Wang, W. L., and Lin, T. I. (2021). Multivariate $t$ semiparametric mixed-effects model for longitudinal data with multiple characteristics.

Journal of Statistical Computation and Simulation, 91(2), 260-281. [pdf]
Zhang, T. (2023). Maximum Likelihood Algorithm for Spatial Generalized Linear Mixed Models without Numerical Evaluations of Intractable Integrals.

Journal of Computational and Graphical Statistics, 32(4), 1636-1648. [pdf] [supplement]

Ito, T., and Sugasawa, S. (2023). Grouped generalized estimating equations for longitudinal data analysis.

Biometrics, 79(3), 1868-1879. [arXiv:2006.06180] [pdf] [supplement]
Kubokawa, T., Sugasawa, S., Tamae, H., and Chaudhuri, S. (2021). General unbiased estimating equations for variance components in linear mixed models.

Japanese Journal of Statistics and Data Science, 4, 841-859. [pdf]
Qu, A., Lindsay, B. G., and Li, B. (2000). Improving generalised estimating equations using quadratic inference functions.

Biometrika, 87(4), 823-836. [pdf]
Xing, Y., Wenqing, M., and Liang, C. (2022). A methodology for improving efficiency estimation based on conditional mix-GEE models in longitudinal studies.

Communications in Statistics-Simulation and Computation, 51(1), 254-265. [pdf]
Ye, H., and Pan, J. (2006). Modelling of covariance structures in generalised estimating equations for longitudinal data.

Biometrika, 93(4), 927-941. [pdf]

Guo, W., Ratcliffe, S. J., and Have, T. T. T. (2004). A random pattern-mixture model for longitudinal data with dropouts.

Journal of the American Statistical Association, 99(468), 929-937. [pdf]
Hogan, J. W., Roy, J., & Korkontzelou, C. (2004). Handling drop‐out in longitudinal studies.

Statistics in Medicine, 23(9), 1455-1497. [pdf]
Lin, H., Fu, B., Qin, G., and Zhu, Z. (2017). Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

Biometrics, 73(4), 1132-1139. [pdf] [supplement] [code]
Yi, G. Y., and He, W. (2009). Median regression models for longitudinal data with dropouts.

Biometrics, 65(2), 618-625. [pdf] [supplement]
Zhou, T., Daniels, M. J., and Müller, P. (2020). A semiparametric Bayesian approach to dropout in longitudinal studies with auxiliary covariates.

Journal of Computational and Graphical Statistics, 29(1), 1-12. [pdf] [supplement] [code]

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