Having graduated from Saint Vincent College in 2014 with a B.S. in Finance and Mathematics, Dr. Justin Petrovich returned to Saint Vincent to teach in 2018. In between, Dr. Petrovich completed his Ph.D. in Statistics at Pennsylvania State University, where he developed and applied methods related to functional data analysis as part of his dissertation. His research continues to focus on methods for functional and longitudinal data. Dr. Petrovich has also served as a statistical consultant for several organizations. Through both his research and consulting experiences he has enjoyed the opportunity to work across a variety of disciplines, including mental health, pediatrics, marketing, and economics.
Dr. Petrovich teaches a variety of courses in statistics, econometrics, data science, and business analytics at Saint Vincent. Since 2022 he is the Chair of the Marketing, Analytics, and Global Commerce Department. Outside of class, he enjoys participating in many of the rich activities offered on campus, from reading groups to lectures and conferences.
Having grown up in Pittsburgh, Dr. Petrovich is a lifelong Steelers and Penguins fan. He was a 4-year runner on Saint Vincent’s Cross Country and Track teams as an undergraduate and continues to enjoy running in his leisure time. He lives in Latrobe with his wife and son and is an active member in the Saint Vincent Basilica Parish.
Petrovich, J., Reimherr, M., & Daymont, C. (2022) Highly Irregular Functional Generalized Linear Regression with Electronic Health Records. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(4), 806–833. https://doi.org/10.1111/rssc.12556.
Hayes, J. A., Petrovich, J., Janis, R. A., Yang, Y., Castonguay, L. G., Locke, B. D. (2020). Suicide among College Students in Psychotherapy: Individual Predictors and Latent Classes. Journal of Counseling Psychology, 67(1), 104–114. http://dx.doi.org/10.1037/cou0000384
Dziak, J., Coffman, D., Reimherr, M., Petrovich, J., Li, R., Shiffman, S., and Shiyko, M. (2019). Scalar-on-Function Regression for Predicting Distal Outcomes from Intensively Gathered Longitudinal Data: Interpretability for Applied Scientists. Statistics Surveys, 13, 150–180. https://doi.org/10.1214/19-SS126
Petrovich, J. and Reimherr, M. (2017). Asymptotic Properties of Principal Component Projections with Repeated Eigenvalues. Statistics and Probability Letters, 130, 42–48. https://doi.org/10.1016/j.spl.2017.07.004