Date on Master's Thesis/Doctoral Dissertation
5-2016
Document Type
Master's Thesis
Degree Name
M.A.
Department
Mathematics
Degree Program
Mathematics, MA
Committee Chair
Gill, Ryan
Committee Co-Chair (if applicable)
Li, Jiaxu
Committee Member
Li, Jiaxu
Committee Member
Kulasekera, K. B.
Author's Keywords
LARS; LASSO
Abstract
Variable selection plays a significant role in statistics. There are many variable selection methods. Forward stagewise regression takes a different approach among those. In this thesis Least Angle Regression (LAR) is discussed in detail. This approach has similar principles as forward stagewise regression but does not suffer from its computational difficulties. By using a small artificial data set and the well-known Longley data set, the LAR algorithm is illustrated in detail and the coefficient profiles are obtained. Furthermore a penalized approach to variable reduction called the LASSO is discussed, and it is shown how to compute its coefficient profiles efficiently using the LAR algorithm with a small modification. Finally, a method called K-fold cross validation used to select the constraint parameter for the LASSO is presented and illustrated with the Longley data.
Recommended Citation
Hettigoda, Sandamala, "Computation of Least Angle Regression coefficient profiles and LASSO estimates." (2016). Electronic Theses and Dissertations. Paper 2404.
https://doi.org/10.18297/etd/2404
