Burcu Eke Rubini

Burcu Eke Rubini

Assistant Professor
Decision Sciences
Phone: (603) 862-1861
Office: Paul College, Room 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 803: Fundamentals of Statistical
  • DS 805: Statistical Learning
  • DS 807: Unstructured Data

Education

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

Research Interests

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

Selected Publications

  • Eke Rubini, B., & Rubini, L. (2024). Gravity with Strategic Behavior .

  • 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

  • Eke Rubini, B., & Rubini, L. (2024). Gravity in Networks: Lessons from the U.S.-China Trade War.

  • Gruji?, 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. (2024, October 16). Mixed Model Imputation for Missing Data in Social Networks. In Women in Statistics and Data Science.

  • Rubini, L., & Eke Rubini, B. (n.d.). Estimating Bilateral Trade Barriers.

  • 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.

  • Yalcinkaya, G., Yeniyurt, S., Kutlubay, O., & Eke Rubini, B. (n.d.). How Businesses Can Leverage Consumer and Expert Reviews to Improve New Product Success.

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  • Most Cited Publications