Carlene Wilson  Greg Evans  Phil Leppard  Julie Syrette 《Risk analysis》2004,24(5):1311-1321

Previous research has reported strong consumer perception that genetically modified (GM) food crops may lead to adverse outcomes in a number of different areas. This is despite the widespread promulgation of the potential benefits and opportunities ascribed to the same technology by many scientists and other experts. A computer-based information gathering and evaluation task was completed by 198 adults to assess the extent to which their initial focus on the dangers or opportunities of genetic modification, or both, could be ascribed to the manner in which they gathered information on the topic (heuristically vs. systematically). Results did not confirm the hypothesis that initial focus (risks, benefits, or both) predicted ongoing information gathering and evaluation behavior. Moreover, also contrary to prediction, most participants primarily used systematic strategies when deriving their initial position, regardless of that opinion. Participants found it difficult to achieve a balanced perspective on GM food crop, even though balanced argument, as measured by order of story selection and time spent reading, was preferred as the source of information. Perceived importance is probably the most influential variable determining information gathering about issues or events to which a level of risk is attached.  相似文献   

    

John A. Ramey  Phil D. Young 《Journal of Statistical Computation and Simulation》2013,83(3):581-596

Classification of gene expression microarray data is important in the diagnosis of diseases such as cancer, but often the analysis of microarray data presents difficult challenges because the gene expression dimension is typically much larger than the sample size. Consequently, classification methods for microarray data often rely on regularization techniques to stabilize the classifier for improved classification performance. In particular, numerous regularization techniques, such as covariance-matrix regularization, are available, which, in practice, lead to a difficult choice of regularization methods. In this paper, we compare the classification performance of five covariance-matrix regularization methods applied to the linear discriminant function using two simulated high-dimensional data sets and five well-known, high-dimensional microarray data sets. In our simulation study, we found the minimum distance empirical Bayes method reported in Srivastava and Kubokawa [Comparison of discrimination methods for high dimensional data, J. Japan Statist. Soc. 37(1) (2007), pp. 123–134], and the new linear discriminant analysis reported in Thomaz, Kitani, and Gillies [A Maximum Uncertainty LDA-based approach for Limited Sample Size problems – with application to Face Recognition, J. Braz. Comput. Soc. 12(1) (2006), pp. 1–12], to perform consistently well and often outperform three other prominent regularization methods. Finally, we conclude with some recommendations for practitioners.  相似文献