Rate My Professor Kaushik Ghosh

Ghosh's research encompasses theoretical developments and practical applications in statistics and biostatistics. His interests include novel applications of statistical methodology in biomedical sciences, such as prediction models for cancer mortality and incidence, joint models for longitudinal and survival data, and clustering genes based on microarray data. Other key areas are disease trend modeling, longitudinal data analysis, Bayesian computations, ranked set sampling, and directional data analysis. Select publications feature Kandhasamy and Ghosh (2017), "Relative risk for HIV in India -- An estimate using conditional auto-regressive models with Bayesian approach," Spatial and Spatio-temporal Epidemiology; Zhu et al. (2012), "Predicting US- and State-Level Cancer Counts for the Current Calendar Year. Part II: Evaluation of Spatiotemporal Projection Methods for Incidence," Cancer; Ghosh and Tiwari (2007), "Prediction of U.S. Cancer Mortality Counts using Semiparametric Bayesian Techniques," Journal of the American Statistical Association; Chacko and Ghosh (2016), "Semi-parametric Bayesian Density Estimation Using Ranked Set Sample in the Presence of Ranking Error," Environmental and Ecological Statistics; and Ghosh (2010), "A New Nonparametric Test for Symmetry," in Advances in Directional and Linear Statistics. He has supervised several Ph.D. students at UNLV, including Sharang Chaudhry (2019), Marvin Javier (2019), Libo Zhou (2014), and Mostafa Shams Esfand Abadi (2022), as per the Mathematics Genealogy Project. Ghosh's publications have received over 789 citations on ResearchGate, reflecting his impact in the field.