Deborah Kunkel

O-Martin 113

School of Mathematical and Statistical Sciences

I am an assistant professor of applied statistics and data science in the School of Mathematical and Statistical Sciences. My research interests include Bayesian statistical methodology, mixture models, clustering, hierarchical models, and applications in cognitive psychology and social sciences.


Papers:

Liu Z., Zhang Z., Kunkel D., Roy-Chaudhury P., Singapogu R. Is Experience in Hemodialysis Cannulation Related to Expertise? A Metrics-based Investigation for Skills Assessment. Ann Biomed Eng. 2021 Jan 8. doi: 10.1007/s10439-020-02708-5. Epub ahead of print. PMID: 33417054.

Kunkel, D. and Peruggia, M. Anchored Bayesian Gaussian Mixture Models. Electronic Journal of Statistics (2020). R code for anchored mixure models .

Kunkel, D., Yan, Z., Peruggia, M., Craigmile, P.F, and Van Zandt, T. Hierarchical hidden Markov models for response time data. Computational Brain & Behavior (2020). Full text online. Supplemental material.

Dominic, J., Tubre, B., Houser, J., Ritter, C., Kunkel, D., Rodeghero, P. Program Comprehension in Virtual Reality. Proc. of the International Conference on Program Comprehension (ICPC'20 ERA), Seoul, Korea, May 23-24, 2020.

Sianko, N., Kunkel, D., Thompson, M.P, Small, M.A., McDonell, J.R. Trajectories of Dating Violence Victimization and Perpetration among Rural Adolescents. Journal of Youth and Adolescence, 1-17 (2019).

Byrne, K., Willis, H., Peters, C., Kunkel, D., Tibbett, T. Behind Closed Doors: The Role of Depressed Affect on Risky Choices Under Time Pressure. Clinical Psychological Science, 8(1), 198-207 (2019).

Kunkel, D. and Peruggia, M. Statistical inference with anchored Bayesian mixture of regressions models: A case study analysis of allometric data. arXiv, (2019).

Kunkel, D., Potter, K., Craigmile, P.F, Peruggia, M., and Van Zandt, T. A Bayesian race model for response times under cyclic stimulus discriminability. Annals of Applied Statistics, 13, 271-296 (2019).

Kunkel D. and Kaizar E. A comparison of existing methods for multiple imputation in individual participant data meta-analysis. Statistics in Medicine, 36, 3507-3532 (2017).


Teaching:

MATH 4030/6030, Introduction to Statistical Theory, Spring 2022.

STAT 8030, Regression and Least Squares Analysis, Fall 2021.

STAT 8020, Statistical Methods II, Summer 2021.

MATH 8820, Intro to Bayesian Statistics , Fall 2020.

MATH 4820, Undergraduate Research , Fall 2020.

DSA 8010, Statistical Methods I , Fall 2020.

STAT 8010, Statistical Methods I, Fall 2018, Spring 2019, Fall 2019, Spring 2020.

STAT 8420, Intro to Statistical Methods, Summer 2019.

STAT 4020/6020, Statistical Computing , Fall 2018.


Collaboration:

I am always interested in collaborating on interdisciplinary projects. Please reach out if you'd like a statistical addition to your research team.

Fall 2019 Statistics Reading group.

Spring 2019 Statistics Reading group.

Data for MATH 3600.

Messy data for MATH 3600.