Author ORCID Identifier
Date Available
7-26-2025
Year of Publication
2023
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Arts and Sciences
Department/School/Program
Statistics
Advisor
Dr. Derek S. Young
Abstract
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)
https://doi.org/10.13023/etd.2023.312
Recommended Citation
Li, Yanxi, "Novel Modelling and Inference Considerations Involving the Exponentially-Modified Gaussian Distribution" (2023). Theses and Dissertations--Statistics. 66.
https://uknowledge.uky.edu/statistics_etds/66
Included in
Applied Statistics Commons, Statistical Methodology Commons, Statistical Models Commons, Statistical Theory Commons
