Paul Mosquin

Paul Mosquin, a statistician at RTI International, has more than 15 years of experience in statistical design and analysis of studies in the social, biological, health, and environmental sciences. He has knowledge of and experience with a wide variety of statistical and epidemiological methods, including survey methods, parametric and nonparametric statistics, and Bayesian and machine learning approaches. He also has managed computing applications, including cluster implementation of parallel computing algorithms, web-interface design, and database development. He has managed and directed projects, supervising up to eight staff members.  

Dr. Mosquin has performed statistical research in sampling theory, for which he developed estimators and sampling designs for adaptive sampling, a type of sampling in which information obtained in a preliminary sample is used to guide later sampling. In the area of Bayesian statistics, he has developed multivariate classification methods by using finite mixtures and Markov chain Monte Carlo (MCMC) methods.