Research Projects
Microbiome
\(\textbf{Koslovsky, M.D.}\) Analyzing Microbiome Data with Taxonomic Misclassification using a Zero-Inflated Dirichlet-Multinomial Model. BMC Bioinformatics, 2025+.
Fu, J.\(^\star\), \(\textbf{Koslovsky, M.D.}\), Neophytou, A.M., and Vannucci, M. A Bayesian Joint Model for Mediation Effect Selection in Compositional Microbiome Data. Statistics in Medicine, 2023+.
\(\textbf{Koslovsky, M.D.}\) A Bayesian Zero-Inflated Dirichlet-Multinomial Regression Model for Multivariate Compositional Count Data. Biometrics: Biometric Methodology, 2023+.
\(\textbf{Koslovsky, M.D.}\) and Vannucci, M. (2020). MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package. BMC Bioinformatics, 21(301).
\(\textbf{Koslovsky, M.D.}\), Hoffman, K., Daniel, C., and Vannucci, M. (2020). A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes. Annals of Applied Statistics, 14(3), 1471-1492.
\(\textbf{Koslovsky, M.D.}\) and Vannucci, M. Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data. Statistical Analysis of Microbiome Data. Springer, Cham, 2021. 249-270.
mHealth
\(\textbf{Koslovsky, M.D.}\), Pettee Gabriel, K., Businelle, M.S., Wetter, D.W., and Kendzor, D.E. Dynamic Functional Variable Selection for Multimodal mHealth Data. In Press: Bayesian Analysis, 2024+.
Liang, M.\(^\star\), \(\textbf{Koslovsky, M.D.}\), H'ebert, E.T., Businelle, M.S., and Vannucci, M., Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors. Bayesian Analysis, 2023+.
Hoskovec, L.\(^\star\), \(\textbf{Koslovsky, M.D.}\), Koehler, K., Peel, J.L., Volckens, J., and Wilson, A. Infinite Hidden Markov Models for Multiple Multivariate Time Series with Missing Data.Biometrics: Practice, 2022
Liang, M.\(^{\star }\), \(\textbf{Koslovsky, M.D.}\), H'{e}bert, E.T., Kendzor, D.E., Businelle, M.S., and Vannucci, M. (2021). Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error. Psychological Methods, 28(4), 880-894.
\(\textbf{Koslovsky, M.D.}\), H'{e}bert, E.T., Businelle, M.S., and Vannucci, M. (2020). A Bayesian Time-Varying Effect Model for Behavioral mHealth Data. Annals of Applied Statistics, 14(4), 1878-1902.
\(\textbf{Koslovsky, M.D.}\), Swartz, M.D., Chan, W., Leon-Novelo, L., Wilkinson, A.V., Kendzor, D.E., and Businelle, M.S. (2018). Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation. Biometrics: Biometric Practice, , 636-644.
Ecology
\(\textbf{Koslovsky, M.D.}\), Kaplan, A, Terranova, V.A., and Hooten, M.B. (2024+). A Unified Bayesian Framework for Modeling Measurement Error in Multinomial Data. In Press: Bayesian Analysis.
Van Ee, J.J.\(^\star\), Hagen, C.A., Pavlacky, D.C., Haukos, D.A., Lawrence, A.J., Tanner, A.M., Grisham, B.A., Fricke, K.A., Rossi, L.G., Beauprez, G.M., Kuklinski, K.E., Matrin, R., \(\textbf{Koslovsky, M.D.}\), Rintz, T.B., and Hooten, M.B. Melded Integrated Population Models. In Press: Journal of Agricultural, Biological, and Environmental Statistics, 2024+.
Van Ee, J.J.\(^\star\), Hagen, C.A., Pavlacky, D.C., Fricke, K.A., \(\textbf{Koslovsky, M.D.}\), and Hooten, M.B. Melding Wildlife Surveys to Improve Conservation Inference. Biometrics: Biometric Practice, 2023+.
Miscellaneous
\(\textbf{Koslovsky, M.D.}\), Swartz, M.D., Leon-Novelo, L., Chan, W., and Wilkinson, A.V. (2018). Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates. Journal of Statistical Computation and Simulation, , 575-596.