Date on Master's Thesis/Doctoral Dissertation
8-2020
Document Type
Master's Thesis
Degree Name
M.A.
Department
Mathematics
Degree Program
Mathematics, MA
Committee Chair
Gill, Ryan
Committee Co-Chair (if applicable)
Han, Dan
Committee Member
Han, Dan
Committee Member
Gaskins, Jeremy
Author's Keywords
linear methods; variables; regression
Abstract
In data sets where there are a small number of observations but a large number of variables observed for each observation, ordinary least squares estimation cannot be used for regression models. There are many alternative including stepwise regression, penalized methods such as ridge regression and the LASSO, and methods based on derived inputs such as principal components regression and partial least squares regression. In this thesis, these five methods are described. K-fold cross validation is also discussed as a way for determining regularization parameters for each method. The performance of these methods in estimation and prediction is also examined through simulation studies under various interesting scenarios. Finally, the methods will be applied to a real data set in which each method is applied to build a model for the weights of mice based on microarray expression data for a large number of genes.
Recommended Citation
Sikder, Rajesh, "Linear methods for regression with small sample sizes relative to the number of variables." (2020). Electronic Theses and Dissertations. Paper 3520.
https://doi.org/10.18297/etd/3520
