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关于举办“应用数学及统计学国际研讨会IV”的通知

  报告题目:Identifying Influential Observations through the Intraclass Correlation Coefficient

  报 告 人: Professor Naijun Sha (University of Texas at El Paso)

  报告内容: We consider the estimation of the intraclass correlation coefficient (ICC) in one-way random effect model. We propose an approach to identify influential observations through the transformation of ICC for diagnostics. Simulation study is conducted to investigate the performance of the methods. We also apply our approach to the applications of real data analysis.
  
  报告时间:2011年6月10日 星期五 上午10:00-11:00  
  
  报告地点:理学院二楼多媒体教室  
  
  报告人简介:

  Research Interests
  Classification and Clustering, Variable Selection Technique, Reliability, Bayesian Approach, Bioinformatics.
  Honors and Awards
  2005-2010, Elected Member, Marquis Who's Who in America.
  Jan. 2004, Elected Member, Academic Keys Who's Who in Sciences Higher Education (WWSHE).
  Apr. 2001, Phi Kappa Phi,  Texas A&M University.
  Select Publications
  1. Kwon, D., Tadesse, M.G., Sha, N., Pfeiffer, R. and Vannucci, M. (2007). Identifying biomarkers from mass spectrometry data with ordinal outcomes. Cancer Informatics, 3, 19-28.
  2. Sha, N., Tadesse, M.G. and Vannucci, M. (2006). Bayesian variable selection for the analysis of microarray data with consored outcomes. Bioinformatics, 22(18), 2262-2268.
  3. Tadesse, M.G., Sha, N., Kim, S. and Vannucci, M. (2006). Identification of biomarkers in classification and clustering of high-throughput data. In Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Mueller and Marina Vannucci (Eds). Cambridge University Press, 97-115.
  4. Tadesse, M., Sha, N. and Vannucci, M. (2005). Bayesian variable selection in clustering high-dimensional data. Journal of American Statistical Association, 100, 602-617.
  5. Sha, N., Vannucci, M., Tadesse, M.G., Brown, P.J., Dragoni, I., Davies, N., Roberts, T.C., Contestabile, A., Salmon, M., Buckley, C. and Falciani, F. (2004). Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage. Biometrics, 60(3), 812-819
                                                  
                                             理学院
                                            2011年6月8日

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