Research Lab:

My research spans multiple areas of statistical methodological development, much of which requires the need for HPC. One large component involves the development of novel finite mixture models and zero-inflated models, both of which require significant computational effort to optimize, with further resources critical for conducting simulation studies. Another significant component involves the construction of tolerance regions for various data structures. This research also requires similar resources for not only their calculation, but for conducting simulation studies to demonstrate their performance.

Project:

Finite Mixtures of Mean-Parameterized Conway-Maxwell-Poisson Models – This project aims at developing a flexible count mixture model that can capture varying degrees of data dispersion. Performance of the optimization method for estimating the model will require well-designed simulation studies, for which HPC will be beneficial.

Computational Method:

Submitting R scripts via sbatch.

Students:

Software:

R, C++ compiler

Collaborators:

None as of now

Grants:

Publications:

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