Research & Scholarship

My research focuses on statistical methodology for regression, variable selection, prediction, bootstrap inference, and machine learning, with additional interests in actuarial science and interdisciplinary applications of statistics.

Research Interests

Linear Regression
Variable Selection
Prediction Intervals
Bootstrap Methods
Machine Learning

Current Research Focus

A central goal of my research is to develop reliable methods for statistical inference after variable selection.

In regression problems, many predictor variables may be available for predicting a response. Variable-selection methods seek a useful subset of those predictors while retaining the information needed for accurate estimation and prediction. My work investigates how uncertainty can be appropriately quantified after this selection process, with particular emphasis on bootstrap methods and prediction intervals.

I am also interested in actuarial science and interdisciplinary applications of statistical and machine-learning methods.

Publications & Scholarly Work

2024
Employing Spectral Signatures in Vegetation to Differentiate Hydrologic Features Above Continuous Permafrost
American Geophysical Union Conference · Abstract published
2021
HRLR Regression
Research in Mathematics & Statistics · April 2021 · with Alan Arnholt and Hasthika S. Rupasinghe
2020
Advertisement for Predictive Model Building
Proceedings of the 32nd Annual International Conference on Technology in Collegiate Mathematics (ICTCM) · with Alan Arnholt and Hasthika Rupasinghe
2020
Comparing Six Shrinkage Estimators with Large Sample Theory and Asymptotically Optimal Prediction Intervals
Statistical Papers · June 2020 · with David J. Olive
2019
A Cosine Approximation to the Skew Normal Distribution
International Mathematical Forum · November 2019 · with Hasthika S. Rupasinghe Arachchige Don and Jose Almer T. Sanqui
2019
Bootstrapping Multiple Linear Regression After Variable Selection
Statistical Papers · April 2019 · with David J. Olive
2019
A Sub-Model Theorem for Ordinary Least Squares
International Journal of Statistics and Probability · January 2019
2018
Examining the n ≥ 30 Is Sufficiently Large for the Sampling Distribution of X̄ to Follow a Normal Distribution Caveat
Bulletin of Statistics and Operations Research · December 2018 · with Alan Arnholt, William Bauldry, and Hasthika Rupasinghe
2018
Sprint Assessment Using Machine Learning and a Wearable Accelerometer
Journal of Applied Biomechanics · with Reed D. Gurchiek, Hasthika R. Arachchige, Ryan S. McGinnis, Alan Arnholt, Herman van Werkhoven, Alan Needle, and Jeffrey M. McBride
2017
Bootstrapping Analogs of Hotelling’s T² Test
Communications in Statistics — Theory and Methods · October 2017 · with Hasthika Rupasinghe
2015
Visualizing and Testing the Multivariate Linear Regression Model
International Journal of Statistics and Probability · January 2015 · with David J. Olive and Hasthika Rupasinghe

Under Review & Work in Progress

Submitted

Large Sample Bootstrap Prediction Intervals for Variable Selection or Shrinkage Estimators

with Hasthika Rupasinghe

Work in Progress

Bootstrap Prediction Regions for Multivariate Regression

with Hasthika Rupasinghe

Grants & Professional Exams

Grants

  • CAS Research/Proposal Development Summer Grant — Received
    2018 Summer Grant Recipients
  • Chancellor’s Innovation Scholars Program — Applied
  • NSF Grant — Statistics — Applied

Society of Actuaries Exams

  • Statistics for Risk Modeling (SRM)
  • Financial Mathematics (FM)
  • Probability (P)

Professional examinations administered by the Society of Actuaries (SOA).