My research interests focus on both statistical applications and theoretical development of statistics. My research interests include nonparametric statistics, likelihood procedures, genomics, epidemiology, and biostatistics. My statistical application work has contributed to the field by developing solutions to some of the complex problems in genomics. Some of the developed statistical test procedures (Mathur and Sadana, 2011; Mathur, 2009; Mathur and Dolo 2007; Bokka and Mathur, 2007; Mathur, Doke, and Sadana, 2006) identify differentially expressed genes using genomics data. These test procedures are efficient as compared to their competitors, and one of the unique qualities of these procedures is that they can be applied even when the sample size is small or when distribution is unknown. My recent work has been in the area of proteomics. The aim of my research is to determine the specific proteins and their abundance and compare them across different experiments (patients), outcomes, and treatment groups, and then find protein markers for the disease.
Proteins play the most important part in the creation and development of cells and tissues, and directing their functions. Identification of the protein signatures of various diseases, especially at their onset will help tremendously in diagnosis and treatment. These protein signatures are particularly important in drug design and clinical trials and help us to understand how the proteins change during a particular treatment or phase of growth. My work will enable researchers in medical field to identify these protein signatures and hence help the researchers to develop targeted drugs and cure the disease in more effective ways. My work in the area of nonparametric inference has focused on bivariate and multivariate location problems. In some of the papers (Mathur and Smith, 2007; Mathur and Dolo, 2007; Bokka and Mathur, 2006; Mathur, 2005; Sen and Mathur, 1997, 2000), we proposed new methodologies for analyzing bivariate data using nonparametric procedures.
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