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Jeremy Gaskins, PhD

Jeremy Gaskins imageBioinformatics and Biostatistics
Associate Professor
485 E. Gray St., Room 130
Louisville, KY 40202
Phone: (502) 852-3300
Fax: (502) 852-3294



Ph.D. (2013) Statistics, University of Florida
B.S. (2007) Mathematics and Applied Mathematics, Auburn University

I joined the Department of Bioinformatics & Biostatistics at UofL in the fall of 2013 at the rank of Assistant Professor and was promoted to Associate Professor with Tenure in July 2019.

Research Interests

My primary research interests include longitudinal data, missing data models, covariance/correlation estimation, joint modeling of mixed data types, Bayesian methodology, and Markov chain Monte Carlo methods.  I also collaborate with the Department of OB/GYN and Women's Health, the Department of Radiation Oncology, and the Commonwealth Institute of Kentucky.


PHST 661 - Probability
PHST 662 - Mathematical Statistics

Selected Publications:

Journal Articles:

Gaskins, J.T., M.J. Daniels. (2013) A nonparametric prior for simultaneous covariance estimation. Biometrika, 100(1): 111-124.

Gaskins, J.T., M.J. Daniels, B.H. Marcus. (2014) Sparsity inducing prior distributions for correlation matrices of longitudinal data. Journal of Computational and Graphical Statistics. 23(4):966-984.

Gaskins, J.T., M.J. Daniels, and B.H. Marcus. (2016) Bayesian methods for non-ignorable dropout in joint models in smoking cessation studies. Journal of the American Statistical Association, 111(516): 1454-1465.

Gaskins, J.T., and M.J. Daniels. (2016) Covariance partition priors: A Bayesian approach to simultaneous covariance estimation for longitudinal data. Journal of Computational and Graphical Statistics. 25(1): 167-186.

Choo-Wosoba, H., J.T. Gaskins, S.M. Levy, S. Datta. (2018) A Bayesian approach for analyzing zero-inflated clustered count data with dispersion. Statistics in Medicine, 37:801-812.

Wu, Y., J.T. Gaskins, M. Kong, S. Datta. (2018) Profiling the Effects of Short Time-course Cold Ischemia on Tumor Protein Phosphorylation using a Bayesian Approach. Biometrics, 74(1): 331-341.

Gaskins, J.T. (2019) Hyper Markov laws for correlation matrices. Statistica Sinica, 29: 165-184.

Sekula, M. J.T. Gaskins, S. Datta. (2019) Detection of differentially expressed genes in discrete single cell RNA sequencing data using a hurdle model with correlated random effects. Biometrics¸ Pre-print available online.

Book Chapters:
Daniels, M.J., and J.T. Gaskins. (2013) Bayesian methods for the analysis of mixed categorical and continuous (incomplete) data. In Analysis of Mixed Data: Methods and Applications (edited by A.R. de Leon and K. Carriere Chough). pg. 189-208. Chapman & Hall/CRC Press

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