Jason A. Osborne, Associate Professor, Dept. of Statistics
Master Black Belt - Design of experiments (mbb_doe),
Areas of Interest
Statistical applications (e.g. coverage processes for particle flow
statistical consulting, statistics in sports
Assessing variability of complex descriptive statistics in Monte Carlo
studies using resampling methods. International Statistical Review.
(2015), (to appear).
A method for detecting hidden additivity in two-factor unreplicated
experiments. Computational Statistics & Data Analysis. (2013),
Mixture models for gene expression experiments with two species. Human Genomics. (2014), 8:12.
Gene selection and cancer type classification of diffuse large-B-cell
lymphoma using a bivariate mixture model for two-species data.
Human Genomics. (2013), 2.
M-estimation of Boolean models for particle flow experiments.
Journal of the Royal Statistics Society Series C-Applied
Statistics. (2009), 58(2):197-210.
Markov chains for the RISK board game revisited. Mathematics
76 (2003) 129-135.
The Sample Lorenz Curve for Goodness-of-fit in the Exponential Order
Model (with T.A. Severini.) Journal of Statistical
Computation and Simulation, 72 (2002) 87-97.
Inference for Exponential Order Statistic Models based on an Integrated
Likelihood Function (with T.A. Severini.) Journal of the
Association, 95 (2000) 1220-1228.
hiddenf an R package to conduct the F-test for hidden additivity.
the False Discovery Rate with SAS