Using prior-data conflict to tune Bayesian regularized regression models

Biziaev, Timofei

Using prior-data conflict to tune Bayesian regularized regression models

dc.contributor.advisor	Chekouo Tekougang, Thierry
dc.contributor.advisor	Kopciuk, Karen A.
dc.contributor.author	Biziaev, Timofei
dc.contributor.committeemember	Deardon, Rob
dc.contributor.committeemember	Evans, Michael
dc.date	2023-11
dc.date.accessioned	2023-10-02T18:27:43Z
dc.date.available	2023-10-02T18:27:43Z
dc.date.issued	2023-09-22
dc.description.abstract	In high-dimensional regression models, variable selection becomes challenging from a computational and theoretical perspective. Bayesian regularized regression via shrinkage priors like the Laplace or spike-and-slab prior are effective methods for variable selection in p > n scenarios provided the shrinkage priors are configured adequately. We propose configuring shrinkage priors using checks for prior-data conflict: tests that assess whether there is disagreement in parameter information provided by the prior and data. We apply our proposed method to the Bayesian LASSO and spike-and-slab shrinkage priors and assess variable selection performance of our prior configurations against competing models through a linear and logistic high-dimensional simulation study. Additionally, we apply our method to proteomic data collected from patients admitted to the Albany Medical Center in Albany NY in April of 2020 with COVID-like respiratory issues. Simulation results suggest our proposed configurations may outperform competing models when the true regression effects are small.
dc.identifier.citation	Biziaev, T. (2023). Using prior-data conflict to tune Bayesian regularized regression models (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117273
dc.language.iso	en
dc.publisher.faculty	Arts
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Biostatistics
dc.subject	Bayesian statistics
dc.subject.classification	Biostatistics
dc.title	Using prior-data conflict to tune Bayesian regularized regression models
dc.type	master thesis
thesis.degree.discipline	Mathematics & Statistics
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.