Date of Graduation
Spring 2019
Degree
Master of Science in Mathematics
Department
Mathematics
Committee Chair
George Mathew
Abstract
An important problem in data science and statistical learning is to predict an outcome based on data collected on several predictor variables. This is generally known as a regression problem. In the field of big data studies, the regression model often depends on a large number of predictor variables. The data scientist is often dealing with the difficult task of determining the most appropriate set of predictor variables to be employed in the regression model. In this thesis we adopt a technique that constraints the coefficient estimates which in effect shrinks the coefficient estimates towards zero. Ridge regression and lasso are two well-known methods for shrinking the coefficients towards zero. These two methods are investigated in this thesis. Ridge regression and lasso techniques are compared by analyzing a real data set for a regression model with a large collection of predictor variables.
Keywords
ridge regression, lasso, cross validation, mean square error, Akaike information criterion, Bayesian information criterion
Subject Categories
Mathematics
Copyright
© Dalip Kumar
Recommended Citation
Kumar, Dalip, "Ridge Regression and Lasso Estimators for Data Analysis" (2019). MSU Graduate Theses. 3380.
https://bearworks.missouristate.edu/theses/3380
