Colloquium/Seminar

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Event(s) on December 2007


  • Tuesday, 11th December, 2007

    Title: Sufficient Dimension Reduction with Application to Microarray Data Analysis
    Speaker: Dr. Lexin Li, Department of Statistics, North Carolina State University, USA
    Time/Place: 11:30  -  12:30
    FSC 1217
    Abstract: Sufficient dimension reduction can aid the analysis of high-dimensional microarray data by transforming the problems to low dimensional projections. The curse of dimensionality is often alleviated, and the informative data visualization may be enabled. In this talk, we start with an application of a dimension reduction method, sliced inverse regression, to a microarray survival data analysis. This exercise also introduces new challenges to the methodology of sufficient dimension reduction, including the presence of highly correlated predictors, the small-n-large-p problem, variable selection in the framework of dimension reduction, and missing data in predictors. We next continue the talk with a discussion of some recently proposed dimension reduction methods to address the above challenges. Some theoretical properties of the proposed methods will be explored, and the analysis of the microarray data will underlie this line of methodology development.


  • Thursday, 13th December, 2007

    Title: 08年中国证券市场政策风险分析
    Speaker: 周斌 博士, 华东师范大学金融与统计学院风险管理与保险系系, China
    Time/Place: 11:30  -  12:30
    FSC 1217
    Abstract: 2007年的中国证券市场发展真可谓波澜壮阔,无论是股指大幅度飚升以及参与证券市场的人数或基金规模的迅速扩张,都达到了中国股市发展中盛况空前的地步。然而,经过股改后的中国证券市场是否能摆脱暴涨暴跌的过程,报告人将从以下两个方面给出回答。 一、中国证券市场发展历程看政策风险 二、08年中国证券市场政策环境分析


  • Monday, 17th December, 2007

    Title: Point Collocation Method – A Powerful Numerical Method
    Speaker: Prof. Alexander Cheng, University of Mississippi, USA
    Time/Place: 09:00  -  10:00
    FSC 1217
    Abstract: Point collocation method has re-emerged as a powerful numerical method, many years after its creation. There are several reasons: - Its formulation is simplest. - It avoids integration; hence the cells/elements needed for integration. - It eliminates the quadrature error introduced by numerical integration. - The introduction of elements leads to local, low degree polynomial, piecewise interpolation, such as the FEM. Limited by the piecewise interpolation, FEM error is algebraic, O(h^k). - Collocation method uses global interpolation, which often leads to exponential error convergence, O(e^{-h^k}). - Its meshless nature making it more flexible solving certain moving boundary problems. This talk discusses the various issues, such as error estimate, stability analysis, and treatment of discontinuity and singularity, that still need to be resolved for the method to be widely adopted.


  • Tuesday, 18th December, 2007

    Title: Evaluation of Reserach Productivity via Data Envelopment Analysis
    Speaker: Prof. Steve Liu, Kent Business School, University of Kent, UK
    Time/Place: 15:00  -  16:00
    FSC 1111
    Abstract: In this talk we first dicuss some general issues of measuring productivity of research units. Then we introduce Data Envelopment Analysis and and discuss its properties. Finally we apply DEA to a case study.