Subgroup-specific regression models
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Yaghoubi, Marjan
issued: 2021-05-31T13:31:58Z
issued: 2021
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Data sets are becoming massive with ever increasing advances in data collection technologies and are altering the nature of biomedical research. With many techniques, these huge data sets can be challenging, or even impossible, to accurately analyse. In biomedical settings, data sets are frequently heterogeneous, with samples representing various subtypes of diseases that are thought to have variations with respect to underlying biology. A motivating example is the study of progressive diseases such as Alzheimer's disease (AD). While there is a significant increase in the number of studies that concentrate on regression modeling of the disease progression, they ignore the fact that the pattern change are profoundly different for patients with various initial pro les. Estimating separate models for each subgroup is extremely difficult due to small sample sizes in the high dimensional setting, but may obtain results that are more accurate and reliable. Moreover, recognizing homogeneous subgroups of predictors can be cumbersome in high-dimensional regression analysis over subgroups of samples. This thesis attempts to improve upon an established method of regularized regression for group-structured datasets by using a linear combination of two penalty functions to select predictive clusters of correlated variables, and to allow for subgroup-specific parameter estimates. In order to showcase the performance of the suggested methodology, we conducted a series of experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset including three groups of Cognitively Normal Controls (CN), Late Mild Cognitive Impairment (LMCI), and Alzheimer's disease (AD) subjects to estimate Mini- Mental State Examination (MMSE) scores in multiple future time points. Results reveal the effectiveness of the suggested method in terms of Root Mean Square Error (RMSE) over several available well-known statistical methods in two subgroups, AD and LMCI. However, in CN group, our proposed …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/78484
note: Subgroup-Specific Regression ModelsbyMarjan YaghoubiB.Sc., University of Science and Culture, 2010M.Sc., Iran University of Science and Technology, 2014A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Mathematics)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)May 2021© Marjan Yaghoubi, 2021The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Subgroup-Specific Regression Modelssubmitted by Marjan Yaghoubi in partial fulfilment of the requirementsof the degree of Master of ScienceDr. Javad Tavakoli, Irving K. Barber Faculty of ScienceSupervisorDr. Paramjit Gill, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Jeffrey Andrews, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Robert Lalonde, Irving K. Barber Faculty of ScienceUniversity ExamineriiAbstractData sets are becoming massive with ever increasing advances in data col-lection technologies and are altering the nature of biomedical research. Withmany techniques, these huge data sets can be challenging, or even impossi-ble, to accurately analyse. In biomedical settings, data sets are frequentlyheterogeneous, with samples representing various subtypes of diseases thatare thought to have variations with respect to underlying biology. A mo-tivating example is the study of progressive diseases such as Alzheimer’sdisease (AD). While there is a significant increase in the number of studiesthat concentrate on regression modeling of the disease progression, they ig-nore the fact that the pattern change are profoundly different for patientswith various initial profiles. Estimating separate models for each subgroupis extremely difficult due to small sample sizes in the high-dimensional set-ting, but may obtain results that are more accurate and reliable. Moreover,recognizing homogeneous subgroups of predictors can be…
has type: Thesis/Dissertation
date issued: 2021-09
is shown at: 10.14288/1.0398202
language: eng
degree discipline: Mathematics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Tavakoli, Javad
type: Text
identifier URI: http://hdl.handle.net/2429/78484