Burcu Eke Rubini

Burcu Eke Rubini

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

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 872: Predictive Analytics
  • ADMN 950: Data Driven Decisions
  • DS 768/898: Forecasting Analytics
  • DS 803: Fundamentals of Statistical
  • DS 805: Statistical Learning
  • DS 807: Unstructured Data

Education

  • 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

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

  • 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., & Rubini, L. (2023, August 5). Trade Networks and Trade Diversion. In Joint Statistical Meetings. Toronto, ON Canada.

  • Eke Rubini, B., & Rubini, L. (2022, August 6). A Mixed Model Approach for Dynamic Trade Networks. In Joint Statistical Meetings 2022. Washington, DC.

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  • Most Cited Publications