Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37061
標題: A robust approach to joint modeling of mean and scale covariance for longitudinal data
作者: Lin, T.I.
林宗儀
Wang, Y.J.
關鍵字: Covariance structure
Maximum likelihood estimates
Reparameterization
Robustness
Outliers
Prediction
multivariate-t distribution
skew-normal-distribution
maximum-likelihood
mixed models
em algorithm
regression
期刊/報告no:: Journal of Statistical Planning and Inference, Volume 139, Issue 9, Page(s) 3013-3026.
摘要: In this paper, we propose a multivariate t regression model with its mean and scale covariance modeled jointly for the analysis of longitudinal data. A modified Cholesky decomposition is adopted to factorize the dependence structure in terms of unconstrained autoregressive and scale innovation parameters. We present three distinct representations of the log-likelihood function of the model and study the associated properties. A computationally efficient Fisher scoring algorithm is developed for carrying out maximum likelihood estimation. The technique for the prediction of future responses in this context is also investigated. The implementation of the proposed methodology is illustrated through two real-life examples and extensive simulation studies. (C) 2009 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/11455/37061
ISSN: 0378-3758
文章連結: http://dx.doi.org/10.1016/j.jspi.2009.02.008
Appears in Collections:統計學研究所

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