Jason A. Osborne, Associate Professor, Dept. of Statistics
Courses (please see moodle for current courses)
- ST556 Advanced Statistical Programming, Spring 2016
- ST431 Design of Experiments, Spring 2016
ST711, Fall, 2013.
Areas of Interest
Statistical applications (e.g. coverage processes for particle flow
statistical consulting, statistics in sports
Exploring interaction effects in two-factor studies using the hiddenf package in R. The R Journal . (2016, to appear).
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