Economics and Decision Sciences

Rong ZhengPhoto of Rong Zheng
Associate Professor of Decision Sciences

Stipes Hall 430F
R-Zheng@wiu.edu

Ph.D., Applied Statistics, University of Alabama, 2017, Some Contributions to Univariate Nonparametric Tests and Control Charts
M.S. Applied Statistics, The University of Alabama
B.S. Mathematics and Applied Mathematics, The Henan University (China)

Dr. Rong Zheng completed her Ph.D. degree at the University of Alabama in 2017. Her educational background is in Applied Statistics and Data Analysis, with research concentration on data classification and model-based clustering, statistical quality control and nonparametric statistics. Her tenure-track faculty position with WIU started in August, 2017. Besides research, she is also highly interested in applicational projects in relevant fields.

Dr. Zheng currently teaches undergraduate and graduate level courses in theoretical statistics, and statistical softwares. She employs both lecture and lab format in her courses. And she is very interested in encouraging and engaging students to participate in academic competition projects.

Research Interests

Statistical Quality Control, Nonparametric Statistics, Finite Mixture Models and Model-based Clustering, Data Mining and Data Analysis, Text Mining.

Selected Research Papers

Sarkar, S., Melnykov, V., Zheng, R. (2020): Gaussian mixture modeling and model-based clustering under measurement inconsistency. Advances in Data Analysis and Classification.


Zheng, R. and Chakraborti, S. (2016): A phase II distribution-free adaptive exponentially weighted moving average chart. Quality Engineering.

Upper Division and Graduate Course Responsibilities

DS303: Applied Business Forecasting and Regression Analysis
DS490(G): Statistical Software for Data Management and Decision Making: R language
DS490(G): Statistical Software for Data Management and Decision Making: SAS language
DS503: Business Statistics for Managerial Decision Making
DS533: Applied Business Forecasting and Planning
DS560: Categorical Data Analysis