My research is focused on building better predictive models through improved estimation of regression parameters. I am interested in the development and application of James-Stein type shrinkage estimation in parametric and semiparametric regression models and their comparisons with penalty estimators. In the event of a trade-off between bias and prediction error, James-Stein-type methods tend to achieve reduced overall prediction error that offsets the bias in the estimation.

Other areas of interest include

  • data mining and machine learning
  • design of experiments
  • longitudinal data analysis
  • Bayesian and MCMC methods
  • missing data analysis
  • application of statistical methods in education, environment, public health and epidemiology