Burcu Eke Rubini
Burcu Eke Rubini is an Assistant Professor (tenure-track) of Decision Sciences at the Peter T. Paul College of Business and Economics, University of New Hampshire. She earned her Ph.D. in Statistics from Arizona State University. Her research and expertise lie in statistical models for complex dependent data, social networks, missing data imputation, and machine learning algorithms and applications.
Courses Taught
- ADMN 420: Business Statistics
- ADMN 510: Business Statistics
- ADMN 872: Predictive Analytics
- ADMN 950: Data Driven Decisions
- ADMN/DS 898/768: Topics/Forecasting Analytics
- DS 768: Forecasting Analytics
- DS 768/898: Forecasting Analytics
- DS 803: Fundamentals of Statistical
- DS 805: Statistical Learning
- DS 807: Unstructured Data
Research Interests
- Nonlinear Statistical Models
- Time Series Analysis
- Statistical Inference
- Business Statistics
- Interpersonal Social Networks
Selected Publications
Zifla, E., & Rubini, B. E. (2024). Multi-criteria evaluation of health news stories. Decision Support Systems, 180, 114187. doi:10.1016/j.dss.2024.114187
Grujić, J., Eke, B., Cabrales, A., Cuesta, J. A., & Sánchez, A. (2012). Three is a crowd in iterated prisoner's dilemmas: experimental evidence on reciprocal behavior.. Sci Rep, 2, 638. doi:10.1038/srep00638
Eke, B., & Kutan, A. M. (2009). Are International Monetary Fund Programs Effective?: Evidence from East European Countries. Eastern European Economics, 47(1), 5-28. doi:10.2753/eee0012-8775470101
Eke, B., & Kutan, A. M. (2005). IMF-Supported Programmes in Transition Economies: Are They Effective?. Comparative Economic Studies, 47(1), 23-40. doi:10.1057/palgrave.ces.8100090
Eke Rubini, B., & Rubini, L. (2022, August 6). A Mixed Model Approach for Dynamic Trade Networks. In Joint Statistical Meetings 2022. Washington, DC.
Eke Rubini, B., & Rubini, L. (2023, August 5). Trade Networks and Trade Diversion. In Joint Statistical Meetings. Toronto, ON Canada.
Eke Rubini, B., & Zifla, E. (2021, October 30). Evaluating Health-Related News Stories: A Mixed Approach that Combines Text Analysis and Machine Learning. In New England chapter of the Association of Information Systems (NEAIS) 2021 Conference. Boston, MA.
Eke Rubini, B. (2020, September 30). Active Learning in Intro Statistics Classroom: Sampling Variability. In Women in Statistics and Data Science. Online.