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Ryan Elmore

Computational Statistician

Ryan Elmore is a computational statistician with the Computational Science Center.

His research has focused primarily on non- and semi-parametric statistical methods related to finite mixture models, density and distribution function estimation, and multivariate data depth. His work involves an almost equal-part mixture of theoretical and computational statistics.

Upon completion of his Ph.D., he worked for 1.5 years at The Australian National University as a postdoctoral research associate with Dr. Peter Hall. Dr. Elmore worked as an Assistant Professor of Statistics at Colorado State University from August 2005 until June 2008. In June 2008, he moved to San Francisco, California, to work at a start-up company, Slide, Inc. He joined NREL as a Computational Statistician in May 2010. His work focuses on uncertainty quantification and sensitivity analysis in computer experiments/simulations. In addition, he consults with a host of groups around NREL on their statistical needs.

Research Interests 

  • Computational statistics
  • Finite mixture models
  • Density estimation
  • Data depth
  • Massive data sets.


  • 2003 Ph.D., statistics, Penn State University

  • 1998 M.S., statistics, Miami University

  • 1995 B.S., mathematics, Morehead State University.

Selected Publications 

  1. Elmore, R.T.; Hettmansperger, T.P.; Xuan, F. (2006). "A fully nonparametric test for one-way layouts." Australian and New Zealand Journal of Statistics (48); pp. 477-490.
  2. Elmore, R.T.; Hall, P.; Troynikov, V.S. (2006). "Nonparametric density estimation from covariate information." Journal of the American Statistical Association (101); pp. 701-711.
  3. Elmore, R.T.; Hettmansperger, T.P.; Xuan, F. (2006). "Spherical Data Depth and a Multivariate Median," Liu, R.Y.; Sefling, R.; Souvaine, D.L., ed. Data Depth: Robust Multivariate Analysis, Computational Geometry, and Applications. Vol. 72, pp. 87-101.
  4. Hall, P.; Neeman, A.; Pakyari, R.; Elmore, R.T. (2005). "Nonparametric inference in multivariate mixtures." Biometrika (92); pp. 667-678.
  5. Elmore, R.T.; Hall, P.; Neeman, A. (2005). "An application of classical invariant theory to identifiability in nonparametric mixtures." Annales de l' Institut Fourier (Grenoble) (55); pp. 1-28.
  6. Elmore, R.T.; Hettmansperger, T.P.; Thomas, H. (2004). "Estimating component cumulative distribution functions in finite mixture models." Communications in Statistics‚ Theory and Methods (33); pp. 1-12.