Graduate Studies
First Advisor
Stephen Kachman
Second Advisor
Qi Zhang
Degree Name
Doctor of Philosophy (Ph.D.)
Committee Members
Kent Eskridge, Susan VanderPlas, Toshihiro Obata
Department
Statistics
Date of this Version
8-2025
Document Type
Dissertation
Citation
A dissertation presented to the Graduate College of the University of Nebraska in partial fulfillment of requirements for the degree of Doctor of Philosophy
Major: Statistics
Under the supervision of Professors Stephen Kachman and Professor Qi Zhang
Lincoln, Nebraska, August 2025
Abstract
We introduced couple different novel approaches to incorporate latent variable information to multivariate mixture regression models with both Gaussian and count data. We also evaluated the performance of these models with existing best approaches with simulated data from various sampling structures and also evaluated one of the models performance with rice metabolite data that provided some novel insights as well as validating existing literature about performance and behavior of these metabolites. We validated the method using extensive simulations and a real-world application. In both quantitative covariate designs and complex treatment design simulations, our method consistently outperformed established tools like limma, edgeR, and DESeq2, demonstrating a superior balance of sensitivity and precision. Application to a rice metabolite dataset confirmed its practical utility, successfully identifying key, biologically-relevant compounds. Through comprehensive simulations, we show this method consistently outperforms established tools in challenging low-replicate studies and complex, multi-group (ANOVA-type) designs, demonstrating superior sensitivity and overall performance.
Advisors: Stephen Kachman and Qi Zhang
Recommended Citation
Hasanthika, Pahalapathirage Dona Kalani, "Multivariate Mixture Regression Models with Known Group Membership and Informative Priors" (2025). Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–. 360.
https://digitalcommons.unl.edu/dissunl/360
Included in
Applied Statistics Commons, Multivariate Analysis Commons, Statistical Methodology Commons
Comments
Copyright 2025, Pahalapathirage Dona Kalani Hasanthika. Used by permission
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