Browsing by Author "Lin, Tsung I."

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Bayesian analysis of hierarchical linear mixed modeling using the multivariate t distribution
(2007-02-01)
Lin, Tsung I.
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Lee, Jack C.
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統計學研究所
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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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Institute of Statistics
This article presents a fully Bayesian approach to modeling incomplete longitudinal data using the t linear mixed model with AR(p) dependence. Markov chain Monte Carlo (MCMC) techniques are implemented for computing posterior distributions of parameters. To facilitate the computation, two types of auxiliary indicator matrices are incorporated into the model. Meanwhile, the constraints on the parameter space arising from the stationarity conditions for the autoregressive parameters are handled by a reparametrization scheme. Bayesian predictive inferences for the future vector are also investigated. An application is illustrated through a real example from a multiple sclerosis clinical trial. (c) 2006 Elsevier B.V. All rights reserved.
Estimation and prediction in linear mixed models with skew-normal random effects for longitudinal data
(2008-04-30)
Lin, Tsung I.
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Lee, Jack C.
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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
This paper extends the classical linear mixed model by considering a multivariate skew-normal assumption for the distribution of random effects. We present an efficient hybrid ECME-NR algorithm for the computation of maximum-likelihood estimates of parameters. A score test statistic for testing the existence of skewness preference among random effects is developed. The technique for the prediction of future responses under this model is also investigated. The methodology is illustrated through an application to Framingham cholesterol data and a simulation study. Copyright (C) 2007 John Wiley & Sons, Ltd.
Finite mixture modelling using the skew normal distribution
(2007-07-01)
Lin, Tsung I.
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Lee, Jack C.
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Yen, Shu Y.
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
Normal mixture models provide the most popular framework for modelling heterogeneity in a population with continuous outcomes arising in a variety of subclasses. In the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this article, we address the problem of analyzing a mixture of skew normal distributions from the likelihood-based and Bayesian perspectives, respectively. Computational techniques using EM-type algorithms are employed for iteratively computing maximum likelihood estimates. Also, a fully Bayesian approach using the Markov chain Monte Carlo method is developed to carry out posterior analyses. Numerical results are illustrated through two examples.
Robust mixture modeling using the skew t distribution
(2007-06-01)
Lin, Tsung I.
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Lee, Jack C.
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Hsieh, Wan J.
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
A finite mixture model using the Student's t distribution has been recognized as a robust extension of normal mixtures. Recently, a mixture of skew normal distributions has been found to be effective in the treatment of heterogeneous data involving asymmetric behaviors across subclasses. In this article, we propose a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings. Statistical mixture modeling based on normal, Student's t and skew normal distributions can be viewed as special cases of the skew t mixture model. We present analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates. The proposed methodology is illustrated by analyzing a real data example.