Hakan Demirtas, PhD
Epidemiology and Biostatistics
Building & Room:
1603 W. Taylor St.
Demirtas, H. & Schafer, J. L. (2003). On the performance of random-coefficient
pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, Volume 22,
Issue 16, 2553-2575.
Demirtas, H. (2005). Multiple imputation under Bayesianly smoothed pattern-mixture
models for non-ignorable drop-out. Statistics in Medicine, Volume 24, Issue 15, 2345-
Demirtas, H. & Hedeker, D. (2007). Gaussianization-based quasi-imputation and
expansion strategies for incomplete correlated binary responses. Statistics in Medicine,
Volume 26, Issue 4, 782-799.
Demirtas, H. & Hedeker, D. (2008). An imputation strategy for incomplete
longitudinal ordinal data. Statistics in Medicine, Volume 27, Issue 20, 4086-4093.
Demirtas, H. & Hedeker, D. (2011). A practical way for computing approximate
lower and upper correlation bounds. American Statistician, Volume 65, Issue 2, 104-109.
Demirtas, H., Hedeker, D. & Mermelstein, R. J. (2012). Simulation of massive public
health data by power polynomials. Statistics in Medicine, Volume 31, Issue 27, 3337-
Demirtas, H. (2016). A note on the relationship between the phi coefficient and the
tetrachoric correlation under nonnormal underlying distributions. American Statistician,
Volume 70, Issue 2, 143-148.
Demirtas, H., Ahmadian, R., Atis, S., Can, F. E. & Ercan, I. (2016). A nonnormal look
at polychoric correlations: Modeling the change in correlations before and after
discretization. Computational Statistics, Volume 31, Issue 4, 1385-1401.
Demirtas, H. & Vardar-Acar, C. (2017). Anatomy of correlational magnitude
transformations in latency and discretization contexts in Monte-Carlo studies (pp. 59-84).
In ICSA Book Series in Statistics, John Dean Chen and Ding-Geng (Din) Chen (Eds):
Monte-Carlo Simulation-Based Statistical Modeling. Singapore: Springer.
Demirtas, H. (2019). Inducing any feasible level of correlation to bivariate data with
any marginals. Forthcoming in American Statistician.