James Booth

Jim Booth
James
Booth
Professor; Director of Graduate Studies
PhD
Department of Statistics and Data Science

I am currently a Professor in the Department of Statistics and Data Science at Cornell University, one of three departments in Computing and Information Science. I visited the Department of Operations Research and Information Engineering at Cornell in 2003, and was hired in the Department of Biological Statistics and Computational Biology, in the College of Agricultural and Life Sciences, the following year. From 1987 to 2003 I was a faculty member in the Department of Statistics at the University of Florida. During that period I spent two years as a Research Fellow at the Australian National University, and one year at Colorado State University. My research interests involve basic statistical methodology including: the bootstrap and Monte Carlo methods, clustering, exact inference, mixed models, generalized linear models, and also applications in bioinformatics. I have taught a variety of courses at Cornell including Statistical Methods II, the second semester of a statistical methods sequence for graduate students from a wide variety of disciplines, Biological Statistics IData Science for All, as well as core courses for statistics undergraduates, professional masters students, and Ph.D. students in the Fields of Statistics. As a CALS faculty member in SDS part of my teaching effort involves contributions to the campus-wide statistical consulting service through the Cornell Statistical Consulting Unit.

Publications

Statistical Methodology

  • Gaynanova, I., Booth, J. G. & Wells, M. T. (2016). Simultaneous sparse estimation of canonical vectors in the p>>n setting. Journal of the American Statistical Association 111, 696–706. Published online 16 Apr 2015.

  • Bar, Booth, Wells (2014), "A bivariate model for simultaneous testing in bioinformatics data", Journal of the American Statistical Association 109(506):537-547.
  • Kormaksson, Booth, Figueroa and Melnick (2012) "Integrative model-based clustering of microarray methylation and expression data". Annals of Applied Statistics 6(3):1327-1347.
  • Bar, Booth, Schifano and Wells (2010), "Laplace approximated EM Microarray Analysis: an empirical Bayes approach for comparative microarray experiments". Statistical Science 25(3):388-407. Please contact Haim Bar at the University of Connecticutt with questions about the Lemma software package.
  • Booth, Federer, Wells and Wolfinger (2009), "A multivariate variance component model for analysis of covariance in designed experiments", Statistical Science 24(2):223-237.

Applications