I joined Clemson in August 2018 as an assistant professor of applied statistics and data science in the School of Mathematical and Statistical Sciences. I graduated from The Ohio State University in 2018 with a PhD in Statistics, advised by Mario Peruggia. My research interests include Bayesian statistical methodology, mixture models, clustering, hierarchical models, and applications in cognitive psychology and social sciences.
School of Mathematical and Statistical Sciences
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).
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 Peruggia, M. Anchored Bayesian Gaussian Mixture Models. arXiv, (2018).
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).
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.
Messy data for MATH 3600.