My research interests lie in the fields of Linear Regression, Variable Selection, Prediction Interval and Bootstraps, and Machine Learning.

The goal of my recent research work is to develop methods for inference after variable selection. The regression model uses several predictor variables to predict a response variable. Variable selection attempts to choose a subset of the predictor variables without any important loss of information. A prediction interval gives an interval of reasonable values for a future value of the response variable given fixed values of the predictor variables.

I am interested in Actuarial science as well.

“Advertisement for Predictive Model Building,” Conference Proceedings, 32nd Annual International Conference on Technology in Collegiate Mathematics (ICTCM), with Alan Arnholt, Hasthika Rupasinghe.

“Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals”, Statistical Papers, June 2020 with David J Olive.

“A cosine approximation to the skew normal distribution”, International Mathematical Forum, November 2019 with Hasthika S. Rupasinghe Arachchige Don, Jose Almer T. Sanqui.

“Bootstrapping Multiple Linear Regression After Variable Selection,” Statistical Papers, April 2019 with David J Olive.

“A Sub-Model Theorem for Ordinary Least Squares”, International Journal of Statistics and Probability, January 2019.

“Examining the \(n\geq 30\) is Sufficiently Large for the Sampling Distribution of \(\bar{X}\) to Follow a Normal Distribution Caveat,” Bulletin of Statistics and Operations Research, December 2018 with Alan Arnholt, William Bauldry, Hasthika Rupasinghe.

“Sprint Assessment using Machine Learning and a Wearable Accelerometer,” Journal of Applied Biomechanics, 2018 with Reed D. Gurchiek, Hasthika R. Arachchige, Ryan S. McGinnis, Alan Arnholt, Herman van Werkhoven, Alan Needle, Jeffrey M. McBride.

“Bootstrapping analogs of the Hotelling’s \(T^2\) test,” Communications in Statistics Theory and Methods, Oct. 17, 2017 with Hasthika Rupasinghe.

“Visualizing and Testing the Multivariate Linear Regression Model,” International Journal of Statistics and Probability, January 22, 2015, with David J Olive, Hasthika Rupasinghe.

“HRLR Regression” submitted, with Alan Arnholt, Hasthika Rupasinghe.

“Large Sample Bootstrap Prediction Intervals for Variable Selection or Shrinkage Estimators” submitted, with Hasthika Rupasinghe.

“Bootstrap Prediction Regions for Multivariate Regression” Work in progress with Hasthika Rupasinghe.

- CAS Research/Proposal Development Summer Grant (Received)
- Chancellor’s Innovation Scholars Program (Applied)
- NSF Grant – Statistics (Applied)

- Sociaty of Actuaries(SOA) Financial Mathematics(FM) exam
- Sociaty of Actuaries(SOA) Probability(P) exam