Author ORCID Identifier

Date Available


Year of Publication


Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation


Arts and Sciences



First Advisor

Dr. Derek S. Young


The exponentially-modified Gaussian (EMG) distribution is well-suited for analyzing data with positive skewness due to its characteristic positive skew from the exponential component. Despite its popularity in various fields, the EMG distribution has only been analyzed for univariate data without any regression settings. To address this limitation, we developed a generalized EMG regression model with covariates by assigning parametric functional forms to some or all of the parameters in the EMG distribution that vary with values of the covariates. To further perform data-clustering on observation points, we propose a competing regression model where the error structure is assumed to be a two-component mixture of a Gaussian and an exponential distribution. We developed various computational routines for parameter estimation, while confidence band construction, model selection processes, and parametric quantile regression procedures are included to validate our methods. The performance of our methods is characterized through extensive Monte Carlo simulations, and we illustrate our novel models using real-world datasets from various disciplines, including neuropsychology and human-computer interaction.

Digital Object Identifier (DOI)

Available for download on Saturday, July 26, 2025