Browsing by Author "Lin, TI"

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A robust approach to t linear mixed models applied to multiple sclerosis data
(2006-04-30)
Lin, TI
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Lee, JC
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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
We discuss a robust extension of linear mixed models based on the multivariate t distribution. Since longitudinal data are successively collected over time and typically tend to be autocorrelated, we employ a parsimonious first-order autoregressive dependence structure for the within-subject errors. A score test statistic for testing the existence of autocorrelation among the within-subject errors is derived. Moreover, we develop an explicit scoring procedure for the maximum likelihood estimation with standard errors as a by-product. The technique for predicting future responses of a subject given past measurements is also investigated. Results are illustrated with real data from a multiple sclerosis clinical trial. Copyright (c) 2005 John Wiley & Sons, Ltd.
Bayesian analysis of Box-Cox transformed linear mixed models with ARMA(p, q) dependence
(2005-08-01)
Lee, JC
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Lin, TI
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Lee, KJ
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Hsu, YL
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統計學研究所
In this paper, we present a Bayesian inference methodology for Box-Cox transformed linear mixed model with ARMA(p, q) errors using approximate Bayesian and Markov chain Monte Carlo methods. Two priors are proposed and put into comparisons in parameter estimation and prediction of future values. The advantages of Bayesian approach over maximum likelihood method are demonstrated by both real and simulated data. (c) 2004 Elsevier B.V. All rights reserved.
On fast supervised learning for normal mixture models with missing information
(2006-06-01)
Lin, TI
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Lee, JC
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Ho, HJ
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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
It is an important research issue to deal with mixture models when missing values occur in the data. In this paper, computational strategies using auxiliary indicator matrices are introduced for efficiently handling mixtures of multivariate normal distributions when the data are missing at random and have an arbitrary missing data pattern, meaning that missing data can occur anywhere. We develop a novel EM algorithm that can dramatically save computation time and be exploited in many applications, such as density estimation, supervised clustering and prediction of missing values. In the aspect of multiple imputations for missing data, we also offer a data augmentation scheme using the Gibbs sampler. Our proposed methodologies are illustrated through some real data sets with varying proportions of missing values. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
On modelling data from degradation sample paths over time
(2003-09-01)
Lin, TI
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Lee, JC
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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
;
Institute of Statistics
This paper is mainly concerned with modelling data from degradation sample paths over time. It uses a general growth curve model with Box-Cox transformation, random effects and ARMA(p, q) dependence to analyse a set of such data. A maximum likelihood estimation procedure for the proposed model is derived and future values are predicted, based on the best linear unbiased prediction. The paper compares the proposed model with a nonlinear degradation model from a prediction point of view. Forecasts of failure times with various data lengths in the sample are also compared.
Shortest service location model for planning waste pickup locations
(2002-05-01)
Kao, JJ
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Lin, TI
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環境工程研究所
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Institute of Environmental Engineering
A municipal solid waste (MSW) and recycled material curbside pickup bus system was recently initiated in a Taiwan city to improve collection service. For such an MSW pickup system, selecting appropriate collection stops critically affects hauling costs and service efficiency. Conventionally, MSW collection points are heuristically and manually chosen, resulting in a hauling system that is not as effective as intended in terms of location suitability and the number of collection points. The Shortest Service Location (SSL) model, which minimizes the sum of service distances, was therefore proposed in this study. The SSL model was compared with two other models for a local MSW pickup system problem. Using georeferenced graphs generated by a geographical information system (GIS) and related programs, the performance of the three models was compared according to walking distance to a service stop, the coverage of a service stop, and the number of service stops. The results show that the SSL solution can shorten walking distances by similar to10% and reduce the overlap of service areas covered.