应用数学及统计学国际研讨会
（International Conference in Applied mathematics and Statistics）
应用数学与应用统计学是自然科学、社会科学、工程科学、医学科学以及生物科学中非常重要的科学工具。本次国际研讨会的目的是建立72886必赢相关研究人员和学者与国内外著名专家、学者、教授在应用数学学科和应用统计学科之间的交流与合作。我们邀请该领域的国内外著名专家、教授做一系列专题报告，对72886必赢应用数学和统计学课题组以及相关学科给予指导并进行合作研究。
Applied mathematics and statistics are very important and scientific tools used in social sciences, natural sciences, engineering, medical and biological sciences, economics and finance, and many other fields in colleges from our university. The purpose of this international conference is to establish cooperation and collaboration between researchers in applied mathematics and statistics from Northwest A and F University and worldwide well-known professors, experts, and researchers. Several professors and experts from domestics and aboard will be invited to deliver colloquium talks, conduct research projects with researchers from our applied and statistics group as well as other related areas.
SERIES TALK IV.
Invited Speaker: Professor Naijun Sha (University of Texas at El Paso)
Title: Identifying Influential Observations through the Intraclass Correlation Coefficient
Abstract: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：72886必赢二楼多媒体教室
报告人简介
Prof. Naijun Sha
Education
Ph.D, Statistics, Texas A&M University, 2002
M.S., Statistics, The University of Texas at El Paso, 1997
B.S., Mathematics, Fudan University, Shanghai, China, 1985
Classification and Clustering, Variable Selection Technique, Reliability, Bayesian Approach, Bioinformatics.
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.
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.
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.
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.
Tadesse, M., Sha, N. and Vannucci, M. (2005). Bayesian variable selection in clustering high-dimensional data. Journal of American Statistical Association, 100, 602-617.
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
72886必赢
2011年5月24日
