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Prof. Boukai works in the general area of mathematical statistics where he has developed parametric and non-parametric methodological frameworks for various statistical inference problems. In sequential analysis, which is his primary area of expertise, he studies the properties of statistical procedures involving inference or estimation based on sequential sampling schemes and associated stopping rules. In recent years he has a growing interest in investigating problems arising from applications involving random effects in nonlinear models. These types of models are often encountered in real-life applications ranging from statistical pharmacokinetics modeling to econometric studies involving auctions of financial assets. This topic often links areas of hierarchical Bayesian modeling with structured parametric modeling and their applications. In this general context, he studies how parameters of such models are affected by random shocks and other such effects and is working on developing novel resampling and recycling schemes that would allow a more accurate evaluation of the small-sample and the large-sample properties of estimates obtained to various models' parameters.