James Booth

I came to Cornell in 2003 as a visitor to the Department of Operations Research and Information Engineering, and was hired in BSCB the following year. From 1987 to 2003 I was a faculty member in the Department of Statistics at the University of Florida. My research has covered a range of statistical topics including the bootstrap and Monte Carlo methods, clustering, exact inference, mixed models, generalized linear models, applications of the saddlepoint and Laplace approximations and model fitting algorithms. More recently I have focused on the development of statistical methods for analyzing modern biological data.

Since coming to Cornell I have taught the Statistical Methods II course for graduate students from a wide variety of disciplines, Theory of Linear and Generalized Linear Models primarily for Ph.D. students in the Department of Statistical Sciences, and special topics courses on Categorical Data Analysis, Likelihood and Bayesian Statistical Methods and Applied Linear Statistical Models. As a faculty member in BSCB, I am also actively involved in the Cornell Statistical Consulting Unit which provides free advice on statistical issues to faculty and students at Cornell.

Teaching Focus

Since I am trained as a statistician, and a faculty member in the Department of Biological Statistics and Computational Biology, my teaching mission is to enhance and support the graduate program in statistics, the undergraduate program in biometry and statistics, and more broadly, statistics education at Cornell. In statistics, as in other fields of study, different topics and levels of instructions present vastly different challenges. Thus, a range of approaches are necessary to be a successful instructor. Courses for statistics Ph.D. students typically emphasize foundational issues, and mathematical theory, as well as the latest methodological developments.

In contrast, courses for non-majors tend to emphasize a wide variety of real life applications, traditional statistical methods, and their implementation using modern computer software. It is therefore important to constantly broaden ones range of interests, and to be abreast of new developments in the field both at a theoretical and a practical level. This is both a major challenge and a key attraction of an academic career.

Research Interests

• Linear models
• Generalized linear models
• Random effects
• Monte Carlo methods

Graduate Fields

• Biometry
• Epidemiology
• Operations Research and Information Engineering
• Statistics