Burcu Eke Rubini

Assistant Professor
Decision Sciences
Phone: (603) 862-1861
Office: Decision Sciences, Paul College Rm 260S, Durham, NH 03824
Pronouns: She/her/hers
Burcu Eke Rubini

Burcu Eke Rubini is an Assistant Professor 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 focuses on statistical models for complex dependent data, social networks, and missing data imputation.


  • Ph.D., Statistics, Arizona State University
  • M.A., Economics and Finance, Southern Illinois University
  • M.S., Economics, Arizona State University
  • B.S., Economics, Middle East Tech Univ

Research Interests

  • Nonlinear Statistical Models
  • Time Series Analysis
  • Statistical Inference
  • Business Statistics
  • Interpersonal Social Networks

Courses Taught

  • ADMN 420: Business Statistics
  • ADMN 510: Business Statistics
  • ADMN 872: Predictive Analytics
  • 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

Selected Publications

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. (2020, September 30). Active Learning in Intro Statistics Classroom: Sampling Variability. In Women in Statistics and Data Science. Online.

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., & 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.

Most Cited Publications