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Department of Statistics

Our Faculty and Staff

Karl Gregory

Title: Assistant Professor
Department: Statistics
College of Arts and Sciences
Email: gregorkb@mailbox.sc.edu
Phone: 803-777-3859
Office: LeConte 219C
Resources: My Website
Curriculum Vitae [pdf]
Department of Statistics
Karl Gregory

Research

My dissertation work centered on bootstrap methodology for time series data and on a two-sample testing problem in which the number of variables exceeded the sample size: The goal of bootstrapping is to estimate the sampling distribution of a sample quantity; the idea is to simulate repeated sampling from the population by repeatedly drawing samples from the sample itself. In the time-series setting, one must be careful to make sure that the resampling scheme preserves the dependence structure of the data. The two-sample problem arose in collaboration with colleagues at the University of Texas MD Anderson Cancer Center and concerned the detection of differences in copy number variation profiles (deletions or duplications of sequences of DNA along the chromosome) between long- and short-surviving glioblastoma multiforme patients.
 
During a two-year post-doc at the Universities of Mannheim and Heidelberg, I began studying nonparametric regression in the high-dimensional setting—when there are a large number of predictors relative to the sample size and each predictor influences the response through a function of unspecified form. In the meantime, I also worked on bootstrap methods for quantile regression in time series. My dissertation work centered on bootstrap methodology for time series data and on a two-sample testing problem in which the number of variables exceeded the sample size: The goal of bootstrapping is to estimate the sampling distribution of a sample quantity; the idea is to simulate repeated sampling from the population by repeatedly drawing samples from the sample itself. In the time-series setting, one must be careful to make sure that the resampling scheme preserves the dependence structure of the data. The two-sample problem arose in collaboration with colleagues at the University of Texas MD Anderson Cancer Center and concerned the detection of differences in copy number variation profiles (deletions or duplications of sequences of DNA along the chromosome) between long- and short-surviving glioblastoma multiforme patients.
 
Since coming to USC in August of 2016, I have begun to work on bootstrap methods for high-dimensional regression problems as well as penalized regression methodologies in pooled testing problems, in which pools of individuals are simultaneously tested for the presence of a disease.

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