Mendoza School of Business

Analytics Research

At the intersection of computer science, statistics, business, and society.


Selected Recent Analytics Publications:

Abbasi, A., Dobolyi, D., Vance, A., & Zahedi, F. M. (2021). The phishing funnel model: A design artifact to predict user susceptibility to phishing websites. Information Systems Research32(2), 410-436.

Abbasi, A., Dobolyi, D., Lalor, J.P., Netemeyer, R., Smith, K., and Yang, Y. (2021) “Constructing a Psychometric Testbed for Fair Natural Language Processing.” In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

Ahmad, F., Abbasi, A., Kitchens, B., Adjeroh, D. A., & Zeng, D. (2020). Deep Learning for Adverse Event Detection from Web Search. IEEE Transactions on Knowledge and Data Engineering.

Ahmad, F., Abbasi, A., Li, J., Dobolyi, D. G., Netemeyer, R. G., Clifford, G. D., & Chen, H. (2020). A Deep Learning Architecture for Psychometric Natural Language Processing. ACM Transactions on Information Systems (TOIS), 38(1), 1-29.

Jiang, F., Zhao, Z., & Shao, X. (2020). Time series analysis of COVID-19 infection curve: A change-point perspective. Journal of econometrics.

Jiang,F., Zhao, Z., Shao, X. (2021) Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model, Journal of the Royal Statistical Society – Series B, with discussion.

Kelley, K., Bilson Darku, F., & Chattopadhyay, B. (2019). Sequential accuracy in parameter estimation for population correlation coefficients. Psychological methods, 24(4), 492.

Lalor, J.P., Yu H. (2020). Dynamic Data Selection for Curriculum Learning via Ability Estimation. Conference on Empirical Methods in Natural Language Processing.

Lalor, J.P., Wu H., Yu H. (2019). Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. Conference on Empirical Methods in Natural Language Processing.

McNeish, D., & Kelley, K. (2019). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods, 24(1), 20.

Rodriguez, P., Barrow, J., Hoyle, A.M., Lalor, J.P., Jia, R. and Boyd-Graber, J., 2021, August. Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 4486-4503).

Shi, P., & Zhao, Z. (2020). Regression for copula-linked compound distributions with applications in modeling aggregate insurance claims. Annals of Applied Statistics, 14(1), 357-380.

Tofighi, D., & Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychological Methods.

Tofighi, D., & Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychological Methods, 25, 496–515.

Tofighi, D., & Kelley, K. (2020). Indirect effects in sequential mediation models: Evaluating methods for hypothesis testing and confidence interval formation. Multivariate Behavioral Research, 55, 188–210.

Traeger, M. L., Sebo, S. S., Jung, M., Scassellati, B., & Christakis, N. A. (2020). Vulnerable robots positively shape human conversational dynamics in a human–robot team. Proceedings of the National Academy of Sciences, 117(12), 6370-6375.

Wang,D., Zhao, Z., Lin, K., Willett, R. (2021) Statistically and computationally efficient change point localization in regression settings, Journal of Machine Learning Research.

Zhao, Z. Shi, P. & Feng, X. (forthcoming) Knowledge Learning of Insurance Risks Using Dependence Models. Journal on Computing,

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