Comparing Bayesian variable selection to lasso approaches for applications in psychology

S.A. Bainter, T.G. McCauley, M.M. Fahmy, Z.T. Goodman, L.B. Kupis, and J.S. Rao
Psychometrika (to appear)
Black and white portrait of J. Sunil Rao

J. Sunil Rao's interests are in applied statistics, biostatistics, small area estimation and machine learning. His current research focuses on cancer, health disparities and opioid relapse. He has also developed software packages with various students and collaborators.

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Research Interests:

High throughput cancer genomic data modeling;
Health disparity estimation;
Opioid relapse prediction;
Machine learning;
High dimensional modeling; Bayesian model selection; Mixed model selection
and prediction;
Small area estimation; Bump/mode hunting;
Robust estimation;
Precision medicine;
Modeling of
pharmacogenomic data


Software Sites


Implements a unified treatment of the “Bump Hunting” task in high-dimensional space.
MOst Recent

Publications and Articles


An information theoretic approach to prevalence estimation and missing data

O. Hossjer, D.A. Pachon-Diaz, C. Zhao and J.S. Rao

Disparities in Survival due to Social Determinants of Health and Access to Treatment in Operable Malignant Pleural Mesothelioma in the United States

A. Alnajar, S.A. Karen, S.S. Razi, J.S. Rao, K. Gawri, G.D. Lopes, D.M. Nguyen, N. Vllamizar and E. Rodriguez
JAMA Open Network (to appear)

Partially Recursively Induced Structured Moderation (PRISM) for modeling racial differences in endometrial cancer survival

J.S. Rao, E. Kobetz, H. Yu, J. Baeker-Bispo and Z. Bailey
J. Sunil Rao, Ph.D.

Professor, Division of Biostatistics
School of Public Health
Director of Biostatistics, Masonic Cancer Center
University of Minnesota, Twin Cities

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