Dan Kowal

Annotations for current/former mentees: * = PhD student, ** = undergraduate student

Submitted

• Kowal, D. Facilitating heterogeneous effect estimation via statistically efficient categorical modifiers.

• *Feldman, J., Reiter, J., and Kowal, D. Gaussian Copula Models for Nonignorable Missing Data Using Auxiliary Marginal Quantiles.

• Kowal, D. Regression with race-modifiers: towards equity and interpretability.

• *Feldman, J. and Kowal, D. Bayesian Quantile Regression with Subset Selection: A Posterior Summarization Perspective.

• *Zito, J. and Kowal, D. The projected dynamic linear model for time series on the sphere.

  • Student Paper Award (Zito): Section on Business and Economic Statistics (2024)

• *Liu, C., Kowal, D., Doss-Gollin, J., and Vannucci, M. Bayesian Functional Graphical Models with Change-Point Detection.

• Kowal, D. and **Wu, B. Semiparametric discrete data regression with Monte Carlo inference and prediction.

Published/In Press

• Kowal, D. and **Wu, B. (2024). Monte Carlo inference for semiparametric Bayesian regression. Journal of the American Statistical Association.

• Bravo, M., Kowal, D., Zephyr, D., *Feldman, J., Ensor, K., and Miranda, M. L. (2024). Spatial variability in relationships between early childhood lead exposure and standardized test scores in 4th grade North Carolina public school students (2013-2016). Environmental Health Perspectives.

• Zhang, S., Morrison, J., *Sun, T., Kowal, D., and Greene, E. (2024). Evaluating integration of letter fragments through contrast and spatially targeted masking. Journal of Vision.

• *Feldman, J. and Kowal, D. (2024). Nonparametric Copula Models for Multivariate, Mixed, and Missing Data. Journal of Machine Learning Research.

  • Student Paper Award (Feldman): Section on Bayesian Statistical Science (2023)

  • Student Paper Award (Feldman): Survey Research Methods, Government Statistics, and Social Statistics Sections (2023, declined)

• *Sun, T. and Kowal, D. (2024). Ultra-efficient MCMC for Bayesian longitudinal functional data analysis. Journal of Computational and Graphical Statistics.

  • Student Paper Award (Sun): Statistical Computing and Statistical Graphics Sections (2024)

• Kowal, D. and Canale, A. (2024). Semiparametric Functional Factor Models with Bayesian Rank Selection. Bayesian Analysis.

  • Lindley Prize: Honorable Mention (2024)

  • Economics, Finance, and Business (EFaB) Presentation Award: European Seminar on Bayesian Econometrics (2021)

• *Gao, Y. and Kowal, D. (2024). Bayesian adaptive and interpretable functional regression for exposure profiles. Annals of Applied Statistics.

  • Student Paper Award (Gao): Section on Bayesian Statistical Science (2023)

• *King, B. and Kowal, D. (2023). Warped Dynamic Linear Models for Time Series of Counts. Bayesian Analysis.

  • Student Paper Award (King): Section on Bayesian Statistical Science (2022)

  • Student Paper Award (King): Business and Economic Statistics Section (2022, declined)

  • Distinguished Student Paper Award (King): International Biometric Society ENAR Spring Meeting (2022)

• *Liu, C., Kowal, D., and Vannucci, M. (2022). Dynamic and Robust Bayesian Graphical Models. Statistics and Computing.

  • Student Paper Award (Liu): Business and Economic Statistics Section (2021)

• Kowal, D. (2022). Subset selection for linear mixed models. Biometrics.

• Kowal, D. and **Wu, B. (2022). Semiparametric count data regression for self-reported mental health. Biometrics.

• *Feldman, J. and Kowal, D. (2022). Bayesian Data Synthesis and the Utility-Risk Trade-Off for Mixed Epidemiological Data. Annals of Applied Statistics.

  • Student Paper Award (Feldman): Health Policy Statistics Section (2022)

• Bravo, M., Zephyr, D., Kowal, D., Ensor, K., and Miranda, M. L. (2022). Racial residential segregation shapes relationships between early childhood lead exposure and 4th grade standardized test scores. Proceedings of the National Academy of Sciences.

• Kowal, D. (2022). Bayesian subset selection and variable importance for interpretable prediction and classification. Journal of Machine Learning Research.

• Karand, J., Reis, K., Ponsiano, S., **Gargurevich, N., **Zhou, J., Fadhil, S., Razac, Y., Rosengard, R., Kowal, D., and Peck, R. (2021). Sex-Dependent Correlates of Arterial Stiffness in Tanzanian Adults. Tropical Medicine & International Health.

• Kowal, D., Bravo, M., Leong, H., Bui, A., Griffin, R. J., Ensor, K. B., and Miranda, M. L. (2021). Bayesian Variable Selection for Understanding Mixtures in Environmental Exposures. Statistics in Medicine.

• Kowal, D. (2021). Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis. Journal of the American Statistical Association.

• Kowal, D. (2021). Dynamic Regression Models for Time-Ordered Functional Data. Bayesian Analysis.

• Kowal, D. and Canale, A. (2020). Simultaneous transformation and rounding (STAR) models for integer-valued data. Electronic Journal of Statistics.

• Miao, Y., Kowal, D., Panchal, N., Vila, J., and Vannucci, M. (2020). Nonlinear state-space modeling approaches to real-time autonomous geosteering. Journal of Petroleum Science and Engineering.

• Kowal, D. and *Bourgeois, D. (2020). Bayesian Function-on-Scalars Regression for High Dimensional Data. Journal of Computational and Graphical Statistics.

• Wu, M., Miao, Y., Panchal, N., Kowal, D., Vannucci, M., Vila, J., and Liang, F. (2019). Stochastic Clustering and Pattern Matching for Real Time Geosteering. Geophysics.

• Kowal, D. (2019). Integer-Valued Functional Data Analysis for Measles Forecasting. Biometrics.

• Kowal, D., Matteson, D.S., and Ruppert, D. (2019). Dynamic Shrinkage Processes. Journal of the Royal Statistical Society, Series B.

  • Student Paper Award: Business and Economic Statistics Section (2018)

  • R package available on GitHub

• Kowal, D., Matteson, D.S., and Ruppert, D. (2017). Functional autoregression for sparsely sampled data. Journal of Business & Economic Statistics.

  • Student Paper Award: Nonparametric Statistics Section (2017)

• Kowal, D., Matteson, D.S., and Ruppert, D. (2017). A Bayesian multivariate functional dynamic linear model. Journal of the American Statistical Association.

  • Student Paper Award: Section on Bayesian Statistical Science (2016)

  • R package available on GitHub

• Kohn, J.C., Chen, A., Cheng, S., Kowal, D., King, M.R., and Reinhart-King, C.A. (2016). Mechanical heterogeneities in the subendothelial matrix develop with age and decrease with exercise. Journal of Biomechanics, 49(9), 1447-1453.

• Alcoser, T.A., Bordeleau, F., Carey, S.P., Lampi, M.C., Kowal, D., Somasegar, S., Varma, S., Shin, S.J., and Reinhart-King, C.A. (2015). Probing the biophysical properties of primary breast tumor-derived fibroblasts. Cellular and Molecular Bioengineering, 8(1), 76-85.

  • Editors’ Choice Award: Cellular and Molecular Bioengineering (2016)

Technical Reports

1. Kowal, D. A Modified Ljung-Box Test for the Functional Linear Model.

2. Kowal, D. and Ding, J. (2012). Applications of linear mixed effect models: an analysis of Missouri school data. Washington U. Senior Honors Thesis Abstracts.

3. Kowal, D. (2009). Methods of capturing stereoscopic movies, their uses, and their limitations. NASA Space Grant Consortium.