Active information and prevalence estimation

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


A statistical technique for detecting differentially expressing genes from microarray data using Bayesian ANOVA

Fence Methods

A class of strategies for selection of fixed and random factors in linear and generalized linear mixed models


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

Publications and Articles


Active information, learning and knowledge acquisition

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

Estimating COVID-19 prevalence from biased samples using imperfect tests: Are we under-valuing the usefulness of rapid antigen testing ?

D.A. Diaz-Pachon, L. Zhou and J.S. Rao

Disparity bump hunting

J.E. Dazard and J.S. Rao
J. Sunil Rao, Ph.D.

Professor and Director, Division of Biostatistics
Miller School of Medicine, University of Miami

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