My research interest in statistics lies in developing inference under a set of relaxed distributional assumptions that may include parametric, nonparametric and robust inference under different sampling conditions. My primary focus is on the construction of sampling designs that increases information content of each measured observations while keeping the sampling cost minimal. To increase the information content of the sample. I usually use available cost-free additional information or auxiliary variables to induce a structure among measured sampling units. This induced structure among measured units under relaxed distributional assumptions leads to more efficient statistical inference and reduces to cost of new discoveries. These sampling designs are particularly appealing in certain finite population settings where auxiliary information may be captured as relative positions (ranks) of population units. I use these population ranks along with probability sampling in finite population settings to insert a strong data structure among measured units and reduce the sampling cost of the study.
Omer Ozturk joined to the statistics faculty in 1996. He currently serves as an associate editor for Environmental and Ecological Statistics, Statistics and Probability Letters, Communications in Statistics- Data Analysis and Simulation, and Communications in Statistics- Theory and Methods. His research was founded by NSA and NSF. He was frequently invited to US Census Bureau as a Summer at Census Scholar. He served as publication officer in the section of nonparametric statistics in ASA. He was Elected fellow of ASA in 2010.