標題: A robust approach to joint modeling of mean and scale covariance for longitudinal data
作者: Lin, Tsung-I
Wang, Yun-Jen
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Covariance structure;Maximum likelihood estimates;Reparameterization;Robustness;Outliers;Prediction
公開日期: 1-Sep-2009
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://dx.doi.org/10.1016/j.jspi.2009.02.008
http://hdl.handle.net/11536/6745
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2009.02.008
期刊: JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume: 139
Issue: 9
起始頁: 3013
結束頁: 3026
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