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James Booth

Jim Booth
James
Booth
Department Chair; Professor
PhD
Department of Statistics and Data Science

I am currently the Department Chair and 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 I, Data 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

  • Booth and Welsh (2020). Generalized regression estimation via the bootstrap. Australian and New Zealand Journal of Statistics, 62(1):5–24.
  • 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.
  • Zipunnikov, Booth and Yoshida (2009). Table counting using the saddlepoint approximation. Journal of Computational and Graphical Statistics, 18(4):915–929
  • Booth, Federer, Wells and Wolfinger (2009), "A multivariate variance component model for analysis of covariance in designed experiments", Statistical Science 24(2):223-237.
  • Booth and Hobert (1999). Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, B 61:265–285

Applications

  • Booth, Hanley, Hodel, Jennelle, Guinness, Them, Mitchell, Ahmed, and Schuler (2023). Sample size for estimating disease prevalence in free-ranging wildlife populations: A Bayesian modeling approach. Journal of Agricultural, Biological and Environmental Statistics. doi:10.1007/s13253-023-00578-7.
  • Hirschl, Booth and Glenna (2023), Religion and climate change indifference: linking the sacred to the social, Review of European Studies, 15(1). doi:10.5539/res.v15n1p11
  • Eilertson, Booth and Bustamante (2012), “SnIPRE: Selection inference using a Poisson random effects model.” PLoS Computational Biology 8(12): e1002806. doi:10.1371/journal.pcbi.1002806.
  • Kormaksson, Booth, Figueroa and Melnick (2012) "Integrative model-based clustering of microarray methylation and expression data". Annals of Applied Statistics 6(3):1327-1347.
  • Caffo and Booth (2003). Monte Carlo conditional tests for log-linear and logistic models: a survey of current methodology. Statistical Methods in Medical Research, 12:1–15.

Contact Info

jim.booth@cornell.edu
1172 Comstock Hall
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