Statistician with areas of competence in statistical modeling, design of experiments (DoE), tests of hypothesis, simulation techniques and quality control tools. Experience in the application of statistics in vaccine development and production. Skilled in SAS and R programming. Exceptional communicator who works well on diverse teams.
Variable Selection in Sparse Ultra-high Dimensional Additive Models
Keywords: nonlinear modeling, high dimensional data, machine learning
Developed a multi-step non-parametric model selection algorithm to select variables in sparse ultra-high dimensional additive models. The algorithm can be used with continuous or discrete response variable, and when the predictors are linearly or nonlinearly related to the response. Simulation results demonstrate that this algorithm is competitive in terms of true and false selection rates and prediction mean squared errors (PMSE). In addition, this algorithm is also used to analyze high throughput data such as genome wide association data.
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