各位老师、同学： 本次共有两场演讲，因此时间提早至 10：00 开始，茶会：10：50。 欢迎参加。 清大统计所 -------------第一场--10：00～10：50---------------------------------- 清华大学、交通大学 统 计 学 研 究 所 专 题 演 讲 题 目: Step-stress tests and some exact inferential results 主讲人: Professor Narayanaswamy Balakrishnan Department of Mathematics and Statistics McMaster University 时 间: 96年11月9日（星期五）10：00 - 10：50 （上午10：50-11：10茶会於统计所821室举行） 地 点: 清大综合三馆837室 Abstract In this talk, I will introduce first various models that are used in the context of step-stress testing. Then, I will describe the cumulative exposure model in detail and describe the model under the assumption of exponentiality. I will then discuss the derivation of the MLEs and their exact conditional distributions, and then present various methods of inference including exact, asymptotic and bootstrap methods and compare their performances. I will develop the results for different forms of censored data, and then present some illustrative examples. Finally, I will point out some open problems in this direction. ---------------第二场--11：00～12：00------------------------------- 清华大学、交通大学 统 计 学 研 究 所 专 题 演 讲 题 目: Covariate-adjusted Matrix Visualization using the Generalized Association Plots Approach 主讲人: 吴汉铭博士 (中研院统计所) 时 间: 96年11月9日（星期五）11：10 - 12：00 （上午10：50-11：10茶会於统计所821室举行） 地 点: 清大综合三馆837室 Abstract In this study, we extended the framework of matrix visualization (MV) by incorporating a covariate adjustment process through the estimation of conditional correlations. The benefit leads directly to the exploration of conditional association structures among the subjects or variables which is not available in the conventional MV. For a discrete covariate, the conditional correlation is estimated by the within and between analysis. This procedure decomposes a correlation matrix into the within- and between-component matrices. The contribution of the covariate effects can then be assessed through the relative structure of the between-component to the original correlation matrix while the within-components act as residuals. When a covariate is of continuous nature, the conditional correlation is equivalent to the partial correlation under the assumption of a joint normal distribution. In addition, a z score map is proposed to identify variable pairs with the most significant differences in correlation before and after a covariate adjustment. Three data sets are employed as examples. The proposed method has been implemented as a module of the GAP package which is available at http://gap.stat.sinica.edu.tw/Software/GAP. 敬请公布 欢迎参加 --

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