Jason A. Osborne, Associate Professor, Dept. of Statistics
CV: osbornenov2016.pdf,
Courses
 ST556 Advanced Statistical Programming, Spring 2016
 ST431 Design of Experiments, Spring 2016

ST422,
Spring, 2014.

ST711,
Fall, 2013.

ST512r,
Fall, 2014.

Xiamen program,
Summer, 2011.
Areas of Interest
Statistical applications (e.g. coverage processes for particle flow
measurement)
statistical consulting, statistics in sports
Software
Some publications

Exploring interaction effects in twofactor 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 twofactor unreplicated
experiments. Computational Statistics & Data Analysis. (2013),
67:95104.

Mixture models for gene expression experiments with two species. Human Genomics. (2014), 8:12.

Gene selection and cancer type classification of diffuse largeBcell
lymphoma using a bivariate mixture model for twospecies data.
Human Genomics. (2013), 2.

Mestimation of Boolean models for particle flow experiments.
Journal of the Royal Statistics Society Series CApplied
Statistics. (2009), 58(2):197210.

Markov chains for the RISK board game revisited. Mathematics
Magazine
76 (2003) 129135.

The Sample Lorenz Curve for Goodnessoffit in the Exponential Order
Statistic
Model (with T.A. Severini.) Journal of Statistical
Computation and Simulation, 72 (2002) 8797.

Inference for Exponential Order Statistic Models based on an Integrated
Likelihood Function (with T.A. Severini.) Journal of the
American Statistical
Association, 95 (2000) 12201228.
Consulting
Software
hiddenf an R package to conduct the Ftest for hidden additivity.
Estimating
the False Discovery Rate with SAS
Other interests