Sunil Mathur, Ph.D. M.Phil., M.Sc., MBA
Associate Professor of Epidemiology and Biostatistics
Robison Hall 222
- Ph.D. (Statistics), M.Phil (Statistics), M.Sc. (Statistics), B.Sc. (Math) University
of Delhi, India
- MBA, Indira Gandhi National University
- Certified training in SAS, SPSS, and SAP, USA
- Certified training in Computer Programming and System Design
- Editorial Board Member, PLOS ONE
- Editorial Board Member, Global Journal of Medicine and Public Health
- Editorial Board Member, Journal of Translational Medicine & Epidemiology
- Associate Editor, International Journal of Statistics and Systems
- Associate Editor, Global Journal of Pure and Applied Mathematics
- Referee for international journals
- Childhood Obesity
- Cancer Communication, Perception, and Prevention
- Public Health
Book: Statistical Bioinformatics with R, Academic Press, USA, January 2010.
Mathur, S.K., and Levy, M. (2013). Association of Lung Cancer Perception and Distress among Smokers, Former Smokers and
Never Smokers: Moderating Role of Physical Activity and Race. Epidemiology Biostatistics and Public Health (Formerly Italian Journal of Public
Health), 10(2), DOI: 10.2427/8839. (Click here to view article)
Mathur, S.K., Levy, M., and Stafford, M.B.R (2013). The Role of Cancer Information Seeking Behavior in Developing and Disseminating Effective
Smoking Cessation Strategies: A Comparison of Current Smokers, Former Smokers and
Never Smokers. Journal of Communication in HealthCare, 6(1), 61-70. (Click here to view article)
Mathur, S. K. and Sepehrifar, M. (2012). A New Signed Rank Test Based on Slopes of Vectors for
Bivariate Location Problems. Statistical Methodology, Accepted for Publication, DOI: 10.1016.
Mathur, S.K., and Sadana, A. (2011). Finding Differentially Expressed Genes in High Dimensional
Data: Rank Based Test Statistic via a Distance Measure. Statistical Methods in Medical Research. Accepted for publication, DOI: 10.1177/0962280211434428.
Mathur, S. K. (2009). A Run based Procedure to Identify Time-lagged Gene Clusters in Microarray
Experiments. Statistics in Medicine, 28 (2), 326-337.
Mathur, S.K. (2009). A New Nonparametric Bivariate Test for Two-Sample Location Problem. Statistical Methods and Applications, 18 (3), 375-388.
Murthy, S.S., Kiran, V.S.R, Mathur, S.K., and Murthy, S. (2008). Noninvasive Transcutaneous Sampling of Glucose by Electroporation.
Journal of Diabetes Science and Technology, 2(2), 250-254.
Mathur, S.K., and Smith, P.F. (2008). An Efficient Nonparametric Test for Bivariate Two-Sample
Location Problem, Statistical Methodology, 5 (2), 142-159.
Mathur, S.K. and Dolo, S. (2008). A new Efficient Statistical Test for Detecting Variability in
the Gene Expression Data, Statistical Methods in Medical Research, 17, 405-419.
Recent Accomplishments and Honors:
Faculty Research Fellow Award, University of Mississippi, 2006, and 2007
First Prize, Sigma Xi Research Poster Competition, University of Mississippi, 2007
Dr. Mathur’s research interests focus on both biostatistical applications and methodological
development of biostatistics. His research interests include childhood obesity, cancer
research, public health, nonparametric statistics, likelihood procedures, genomics,
biostatistics, and epidemiology. In the area of pediatric obesity, the number of children
who are overweight and obese is increasing rapidly world-wide, and the World Health
Organization has declared obesity as one of the largest epidemics of modern times.
In order to combat the epidemic of childhood obesity, there is a pressing need to
understand the association among different factors (genetic and non-genetic) and childhood
obesity. We are working to establish the association of several factors and obesity
among children and examine the moderating role of birth weight, race, and maternal
smoking status during pregnancy. Dr. Mathur is working in the area of lung cancer
which is a second most common cancer among men and women (American Cancer Society,
2011), making it as a leading cause of death in the Unites States. He is exploring
the association of lung cancer perception and other factors and examining the effects
of physical activity and race on this association. He is also exploring other factors
that may affect risk perception of lung cancer. In genomics, his application work
is motivated by the recent developments in the analysis of genomic data where the
sample sizes are usually very small and it is hard to make any distributional assumptions
regarding the underlying data. Through his application work, he has found solutions
to some of the complex problems in genomics, and developed statistical test procedures
to identify differentially expressed genes. 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 distribution is
unknown. In the nonparametric inference, he has been working on bivariate and multivariate
location problems. In some of the papers, he proposed new methodologies for analyzing
bivariate data using nonparametric procedures. He gives importance to setting up successful
collaborations, and always welcomes new collaboration opportunities.