Browsing by Author "Lin, Tsung-I"

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A robust approach to joint modeling of mean and scale covariance for longitudinal data
(2009-09-01)
Lin, Tsung-I
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Wang, Yun-Jen
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
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.
Supervised learning of multivariate skew normal mixture models with missing information
(2010-06-01)
Lin, Tzy-Chy
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Lin, Tsung-I
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統計學研究所
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Institute of Statistics
We establish computationally flexible tools for the analysis of multivariate skew normal mixtures when missing values occur in data. To facilitate the computation and simplify the theoretical derivation, two auxiliary permutation matrices are incorporated into the model for the determination of observed and missing components of each observation and are manifestly effective in reducing the computational complexity. We present an analytically feasible EM algorithm for the supervised learning of parameters as well as missing observations. The proposed mixture analyzer, including the most commonly used Gaussian mixtures as a special case, allows practitioners to handle incomplete multivariate data sets in a wide range of considerations. The methodology is illustrated through a real data set with varying proportions of synthetic missing values generated by MCAR and MAR mechanisms and shown to perform well on classification tasks.