1 |
Agresti, A. (2002). Categorical Data Analysis, 2nd ed., Wiley and Sons, New York.
|
2 |
Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 125-134.
|
3 |
Choi, N. and Huh, J. (2014). A longitudinal study for child aggression with Korea welfare panel study data. Journal of the Korean Data & Information Science Society, 25, 1439-1447.
DOI
|
4 |
Daniels, M. J. and Pourahmadi, M. (2002). Bayesian analysis of covariance matrices and dynamic models for longitudinal data. Biometrika, 89, 553-566.
DOI
|
5 |
Daniels, M. J. and Pourahmadi, M. (2009). Modelling covariance matrices via partial autocorrelations. Journal of Multivariate Analysis, 100, 2352-2363.
DOI
|
6 |
Daniels, M. J. and Zhao, Y. D. (2003). Modeling repeated count data subject to informative dropout. Statistics in Medicine, 22, 1631-1647.
DOI
|
7 |
Fitzmaurice, G. M. and Laird, N. M. (1993). A likelihood-based method for analysing longitudinal binary response. Biometrika, 80, 141-151.
DOI
|
8 |
Heagerty, P. J. (1999). Marginally specied logistic-normal models for longitudinal binary data. Biometrics, 55, 688-698.
DOI
|
9 |
Heagerty, P. J. (2002). Marginalized transition models and likelihood inference for longitudinal categorical data. Biometrics, 58, 342-351.
DOI
|
10 |
Heagerty, P. J. and Kurland, B. F. (2001). Misspecified maximum likelihood estimates and generalised linear mixed models. Biometrika, 88, 973-985.
DOI
|
11 |
Jeon, J. and Lee, K. (2014). Review and discussion of marginalized random effects models. Journal of the Korean Data & Information Science Society, 25, 1263-1272.
DOI
|
12 |
Joe, H. (2006). Generating random correlation matrices based on partial correlations. Journal of Multivariate Analysis, 97, 2177-2189.
DOI
|
13 |
Lee, K. and Daniels, M. J. (2008). Marginalized models for longitudinal ordinal data with application to quality of life studies. Statistics in Medicine, 27, 4359-4380.
DOI
|
14 |
Lee, K., Joo, Y., Yoo, J. K. and Lee, J. (2009). Marginalized random effects models for multivariate longitudinal binary data. Statistics in Medicine, 28, 1287-1300.
|
15 |
Lee, K. and Mercante, D. (2010). Longitudinal nominal data analysis using marginalized models. Computational Statistics and Data Analysis, 54, 208-218.
DOI
|
16 |
Lee, K., Kang, S., Liu, X. and Seo, D. (2011). Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models. Journal of Applied Statistics, 38, 1577-1590.
DOI
|
17 |
Pan, J. and MacKenzie, G. (2007). Modelling conditional covariance in the linear mixed model. Statistical Modelling, 7, 49-71.
DOI
|
18 |
Lee, K., Lee, J., Hagan, J and Yoo, J. K. (2012). Modelling the random effects covariance matrix for generalized linear mixed models. Computational Statistics and Data Analysis, 56, 1545-1551.
DOI
|
19 |
Lee, K., Daniels, M. J. and Joo, Y. (2013). Flexible marginalized models for bivariate longitudinal ordinal data. Biostatistics, 14, 462-476.
DOI
|
20 |
Pan, J. and MacKenzie, G. (2003). On modelling mean-covariance structure in longitudinal studies. Biometrika, 90, 239-244.
DOI
|
21 |
Pourahmadi, M. (1999). Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation. Biometrika, 86, 677-690.
DOI
|
22 |
Pourahmadi, M. (2000). Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix. Biometrika, 87, 425-435.
DOI
|
23 |
Wang, Y. and Daniels, M. J. (2013). Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances. Journal of Multivariate Analysis, 116, 130-140.
DOI
|
24 |
Wannamethee, S. G., Shaper, A. G., Lennon, L., Morris, R. W. (2006). Metabolic syndrome vs Framingham risk score for prediction of coronary heart disease, stroke, and type 2 diabetes mellitus. Journal of the American Medical Association, 295 819-821.
DOI
|