Demirtas, H. & Schafer, J. L. (2003). On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, 22 (16), 2553-2575.
Demirtas, H. (2005). Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, 24 (15), 2345-2363.
Demirtas, H. & Hedeker, D. (2007). Gaussianization-based quasi-imputation and expansion strategies for incomplete correlated binary responses. Statistics in Medicine, 26 (4), 782-799.
Demirtas, H. & Hedeker, D. (2008). An imputation strategy for incomplete longitudinal ordinal data. Statistics in Medicine, 27 (20), 4086-4093.
Demirtas, H. & Hedeker, D. (2011). A practical way for computing approximate lower and upper correlation bounds. American Statistician, 65 (2), 104-109.
Demirtas, H., Hedeker, D. & Mermelstein, R. J. (2012). Simulation of massive public health data by power polynomials. Statistics in Medicine, 31 (27), 3337-3346.
Demirtas, H. (2016). A note on the relationship between the phi coefficient and the tetrachoric correlation under non-normal underlying distributions. American Statistician, 70 (2), 143-148.
Demirtas, H., Ahmadian, R., Atis, S., Can, F. E. & Ercan, I. (2016). A non-normal look at polychoric correlations: Modeling the change in correlations before and after discretization. Computational Statistics, 31 (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, J. D. Chen & D.-G. (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.xxx American Statistician. DOI: 10.1080/00031305.2017.1379438.
