Browsing by Author "Chen, Pei-Chun"

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A simple Bayesian mixture model with a hybrid procedure for genome-wide association studies
(2010-08-01)
Wei, Yu-Chung
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Wen, Shu-Hui
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Chen, Pei-Chun
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Wang, Chih-Hao
;
Hsiao, Chuhsing K.
Genome-wide association studies often face the undesirable result of either failing to detect any influential markers at all because of a stringent level for testing error corrections or encountering difficulty in quantifying the importance of markers by their P-values. Advocates of estimation procedures prefer to estimate the proportion of association rather than test significance to avoid overinterpretation. Here, we adopt a Bayesian hierarchical mixture model to estimate directly the proportion of influential markers, and then proceed to a selection procedure based on the Bayes factor (BF). This mixture model is able to accommodate different sources of dependence in the data through only a few parameters. Specifically, we focus on a standardized risk measure of unit variance so that fewer parameters are involved in inference. The expected value of this measure follows a mixture distribution with a mixing probability of association, and it is robust to minor allele frequencies. Furthermore, to select promising markers, we use the magnitude of the BF to represent the strength of evidence in support of the association between markers and disease. We demonstrate this procedure both with simulations and with SNP data from studies on rheumatoid arthritis, coronary artery disease, and Crohn's disease obtained from the Wellcome Trust Case-Control Consortium. This Bayesian procedure outperforms other existing methods in terms of accuracy, power, and computational efficiency. The R code that implements this method is available at http://homepage.ntu.edu.tw/similar to ckhsiao/Bmix/Bmix.htm. European Journal of Human Genetics (2010) 18, 942-947; doi:10.1038/ejhg.2010.51; published online 21 April 2010
An RFID application in the food supply chain: A case study of convenience stores in Taiwan
(2011-09-01)
Hong, I-Hsuan
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Dang, Jr-Fong
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Tsai, Yi-Hsuan
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Liu, Chen-Shen
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Lee, Wang-Tsang
Food hazards can appear at any stage of global food supply chains, making it essential to define critical control points to capture the data about ingredients, manufacture and dates-certain (sell-by, use-by), etc., and provide it in a transparent manner to supply chain participants and consumers. The government of Taiwan has appointed a non-profit research organization to conduct a pilot project to launch a potential national-wide food traceability system to increase the intangible value of purchased food and to enhance food safety. This paper discusses a financially viable business model for a Radio Frequency Identification (REID) application to a food traceability system. We conduct a case study of REID implementation in the chain of convenience stores in Taiwan. The Taiwanese experiment may have implications for policy-makers, industry and public health officials elsewhere. (C) 2011 Elsevier Ltd. All rights reserved.
Multiclass support vector classification via coding and regression
(2010-03-01)
Chen, Pei-Chun
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Lee, Kuang-Yao
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Lee, Tsung-Ju
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Lee, Yuh-Jye
;
Huang, Su-Yun
The multiclass classification problem is considered and resolved through coding and regression. There are various coding schemes for transforming class labels into response scores. An equivalence notion of coding schemes is developed, and the regression approach is adopted for extracting a low-dimensional discriminant feature subspace. This feature subspace can be a linear subspace of the column span of original input data or kernel-mapped feature data. The classification training and prediction are carried out in this feature subspace using a linear classifier, which lead to a simple and computationally light but yet powerful toolkit for classification. Experimental results, including prediction ability and CPU time comparison with LIBSVM, show that the regression-based approach is a competent alternative for the multiclass problem. (C) 2009 Elsevier B.V. All rights reserved.
Strengthening knowledge sharing and job dedication The roles of corporate social responsibility and ethical leadership
(1970-01-01)
Liu, Min-Ling
;
Lin, Chieh-Peng
;
Chen, Mei-Liang
;
Chen, Pei-Chun
;
Chen, Kuang-Jung
Purpose The purpose of this paper is to propose a moderated mediation model to explain how corporate social responsibility (CSR) and ethical leadership influence knowledge sharing and job dedication through the mediating mechanism of positive affective tone and cognitive meaningfulness. Design/methodology/approach The research hypotheses were empirically tested using a survey of employees from the high-tech industry in Taiwan. The research constructs in this study were measured using five-point Likert scales modified from existing literature. The survey data were empirically analyzed with two-step structural equation modeling (SEM) and regression analysis. Findings The empirical results of this study reveal that CSR and ethical leadership positively relate to positive affective tone and cognitive meaningfulness. Knowledge sharing is positively affected by positive affective tone whereas job dedication is positively affected by positive affective tone and cognitive meaningfulness. While the relationship between positive affective tone and job dedication is positively moderated by job demand, the relationship between cognitive meaningfulness and job dedication is negatively moderated by job demand. Originality/value This study elucidates the ethical influences from organizations (i.e. CSR) and leaders (i.e. ethical leadership) respectively to explain affective and cognitive processes involved in work situations. At the same time, by clarifying the moderating role of job demand, this study provides valuable implications for managers to effectively leverage job demand for increasing employees' job dedication.