• Research Associate/
  Assistant Professor
  
• PhD, Penn State University 2008

Trent Gaugler received a B.S. in Mathematics from Bucknell University, graduating Magna Cum Laude in 2003 and he received his Ph.D. in Statistics (with Biostatistics Option) from Penn State University in 2008. Trent has joined the Department as Research Associate/Assistant Professor of Statistics. His appointment is divided between the Statistical Consulting Center and the Department of Statistics with duties including teaching and collaborative research activities.

Research Interests

Trent’s main research interests lie in survival analysis, resampling methods, linear models, nonparametric statistics and Neyman-Scott asymptotics. His Ph.D. research involved a new fully nonparametric model for the 2-way crossed mixed effects design, where asymptotic distributions for the test statistics were derived under the Neyman-Scott framework. His current research focuses on extending these results to other linear models, including the random effects model, nested models, and models that are capable of handling repeated measures or missing/censored data.

The main goal of his research is to develop linear models that are robust to the classical framework, where assumptions of normality, homoscedasticity, and uncorrelated effects, as well as the convention of balanced designs, are necessary. He also develops these models under the unconventional large p, small n (Neyman-Scott) framework, which is of interest in a variety of fields, including Bioinformatics. Much of his research has led to asymptotic distributions that do not have closed forms, which leads to a natural interest in resampling methods.

Specifically, Trent is currently interested in bootstrapping U-statistics, while his earlier research focused on bootstrap and permutation tests for parameters of survival time distributions.

Last updated: October 2008