Research

My main areas of research interest to date have been twofold. First, I am interested in multivariate
statistical methods that deal with robust estimation and outlier detection. The second major area of my research is in statistical genetics, speci fically the identi fication rare variant associations with complex traits.

Recent Publications:

  • A.S. Turkmen and S. Lin Detecting X-linked common and rare variant effects in family-based sequencing studies–[FxSKAT]
  • A.S. Turkmen, Y. Yuan, and N. Billor (2019) “Evaluation of Methods for Adjusting Population
    Strati cation in Genome-wide Association Studies: Standard versus Categorical Principal Component
    Analysis,” Annals of Human Genetics, 83(6), 454-464.
  • N. Billor, and A.S. Turkmen. (2019) “Emergence of Statistical Methodologies in Twenty-First Century,”
    Women in Industrial and Systems Engineering: Key Advances and Perspectives on Emerging
    Topics, 27-48 [book chapter, editor-reviewed]
  • Y. Yuan, N. Billor, and A.S. Turkmen. (2018) “Detecting Outliers in SNP Data Using Benford’s
    Law,” JSM Proceedings, 2365-2376 [non-refereed]
  • A.S. Turkmen and S. Lin “Are Rare Variants Really Independent?” Genetic Epidemiology, to appear.
  • O.  Ozturk and A.S. Turkmen (2016). “Distribution Free Quantile Inference Based on Clustered Data,” Metrika, 79(7), 867-893
  • A.S. Turkmen and O.  Ozturk (2016). “Generalized Rank Regression Estimator with Standard Error Adjusted Adaptive Lasso,” Australian & New Zealand Journal of Statistics, 58(1), 121-135
  • A.S. Turkmen and G. Tabakan (2015). “Robust Difference Based Estimation in Semiparametric Partially Linear Models” Communications in Statistics: Simulation and Computation, 44(2), 417-432
  • A.S. Turkmen, Z. Yan, Y.Q. Hu, and S. Lin (2015). “Kullback-Leibler Distance Methods for Detecting Disease Association with Rare Variants for Sequencing Data,”  Annals of Human Genetics, 79(3):199-208.
  • A.S. Turkmen and O. Ozturk (2014) “Rank Based Shrinkage Estimation in Multiple Linear Regression,” Journal of Nonparametric Statistics, 26(4), 737-754
  • G. Satten, B. , S. Biswas, C. Papachristou, A.S. Turkmen, and I.R.  Konig, (2014). “Population-based Association and Gene by Environment Interactions in the Genetic Analysis Workshop 18,” Genetic Epidemiology, 38, S1: S49-56
  • A.S. Turkmen, and S. Lin (2014). “Identifying Rare Variant Associations in Population and Family-based Designs,” BMC Proceedings, (8): S58
  • A.S. Turkmen, and S. Lin (2014). “Blocking Approach for Identification of Rare Variants in Family-Based Association Studies,” PLoS One, 9(1), e86126
  • A.S. Turkmen, and N. Billor (2013). “Influence Function Analysis for the Robust Partial Least Squares (RoPLS) Estimator,” Communications in Statistics: Theory and Methods, 42, 2818-2836
  • A.S. Turkmen, and N. Billor (2013). “Partial Least Squares Classification for High Dimensional Data Using the PCOUT Algorithm,” Computational Statistics, 28(2), 771-788
  • A.S. Turkmen, and S. Lin (2012). “An Optimum Projection and Noise Reduction Approach for Detecting Rare and Common Variants Associated with Complex Diseases,” Human Heredity, 74(1):51-60
  • A.S. Turkmen, and S. Lin (2011). “Gene-Based Partial Least Squares Approach for Detecting Rare Variants Associations with Complex Traits,” BMC Proceedings, (5), S19