An Overview of Multivariate Regression Analysis of Biological Data

Honors College Thesis

Public Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/honors_college_theses/g445ck874

Descriptions

Attribute Name	Values
Creator	DeHart, Christopher M.
Abstract	This study investigates methods of multivariate regression analysis and their applications to biological data. It provides an overview of methods of reduced rank regression and sparse multivariate regression, including their motivations, methods, and applications. It also implements a form of sparse regression known as Regularized Multivariate Regression for Identifying Master Predictors to identify top master predictors for miRNA regulation of mRNA where a master predictor is defined by a predictor variable that affects many response variables. The data used in this study is from The Cancer Genome Atlas Project and samples from breast cancer patients. Key Words: Statistics, Biology, Multivariate Analysis
Resource Type	Honors College Thesis
Date Issued	2019-06-05
Degree Level	Bachelor's
Degree Name	Honors Bachelor of Science (H.B.S.)
Degree Field	Mathematics Economics
Degree Grantor	Oregon State University
Commencement Year	2019
Advisor	Jiang, Duo
Committee Member	Emerson, Sarah Jiang, Yuan
Non-Academic Affiliation	Oregon State University. Honors College
Rights Statement	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]

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