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	Mixture regression for longitudinal data based on joint mean–covariance model 期刊论文 Journal of Multivariate Analysis,2022, 卷号: 190 作者: Yu, Jing; Nummi, Tapio; Pan, Jianxin 收藏 \| 浏览/下载：54/0 \| 提交时间：2022/02/23 Finite mixture models Heterogeneity Joint mean–covariance model Modified Cholesky decomposition Progression trajectory
	Regularized estimation of the Mahalanobis distance based on modified Cholesky decomposition 期刊论文 Communications in Statistics Case Studies Data Analysis and Applications,2022 作者: Dai, Deliang; Pan, Jianxin; Liang, Yuli 收藏 \| 浏览/下载：16/0 \| 提交时间：2022/10/21 Mahalanobis distance Modified Cholesky decomposition outlier detection regularization smoothing
	Random effects selection in generalized linear mixed models via shrinkage penalty function 期刊论文 Statistics and Computing,2014, 卷号: 24, 期号: 5, 页码: 725-738 作者: Pan, Jianxin; Huang, Chao 收藏 \| 浏览/下载：10/0 \| 提交时间：2021/08/16 Generalized linear mixed models Modified Cholesky decomposition Penalized quasi-likelihood Penalty function Random effects
	Nonparametric estimation of mean and covariance structures for longitudinal data 期刊论文 Canadian Journal of Statistics,2013, 卷号: 41, 期号: 4, 页码: 557-574 作者: Lin, Huazhen; Pan, Jianxin 收藏 \| 浏览/下载：12/0 \| 提交时间：2021/08/16 Modified Cholesky decomposition Modified local linear smoothing method Non-parametric mean-covariance models Single index models
	Semiparametric mean–covariance regression analysis for longitudinal data 期刊论文 Journal of the American Statistical Association,2010, 卷号: 105, 期号: 489, 页码: 181-193 作者: Leng, Chenlei; Zhang, Weiping; Pan, Jianxin 收藏 \| 浏览/下载：11/0 \| 提交时间：2021/08/16 Covariance misspecification Efficiency Generalized estimating equation Longitudinal data Modified Cholesky decomposition Semiparametric models
	Nonparametric Regression of Covariance Structures in Longitudinal Studies 期刊论文 MIMS EPrint,2009, 页码: 1-34 作者: Pan, Jianxin; Ye, Huajun; Li, Runze 收藏 \| 浏览/下载：10/0 \| 提交时间：2022/02/22 Covariance modelling Local likelihood method Longitudinal studies Modified Cholesky decomposition Modified cross validation with leave-one-subject-out Nonparametric regression