Title page for ETD etd-011410-181727

Document Type masters report

Author Name He, Shiquan

URN etd-011410-181727

Title A Review of Linear Regression and some Basic Proofs for Lasso

Degree MS

Department Mathematical Sciences

Advisors
Zheyang Wu, Advisor
Bogdan M. Vernescu, Department Head

Keywords
Linear regression
lasso

Date of Presentation/Defense 2010-01-14

Availability unrestricted

Abstract
The goal of this paper is to do some basic proofs for lasso and have a deep understanding of linear regression. In this paper, firstly I give a review of methods in linear regression, and most concerns with the method of lasso. Lasso for ‘least absolute shrinkage and selection operator’ is a regularized version of method adds a constraint which uses norm less or equal to a given value t. By doing so, some predictor coefficients would be shrank and some others might be set to 0. We can attain good interpretation and prediction accuracy by using lasso method. Secondly, I provide some basic proofs for lasso, which would be very helpful in understanding lasso. Additionally, some geometric graphs are also given and one example is illustrated.

Files
he.pdf

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Document Type	masters report
Author Name	He, Shiquan
URN	etd-011410-181727
Title	A Review of Linear Regression and some Basic Proofs for Lasso
Degree	MS
Department	Mathematical Sciences
Advisors	Zheyang Wu, Advisor Bogdan M. Vernescu, Department Head
Keywords	Linear regression lasso
Date of Presentation/Defense	2010-01-14
Availability	unrestricted
Abstract The goal of this paper is to do some basic proofs for lasso and have a deep understanding of linear regression. In this paper, firstly I give a review of methods in linear regression, and most concerns with the method of lasso. Lasso for ‘least absolute shrinkage and selection operator’ is a regularized version of method adds a constraint which uses norm less or equal to a given value t. By doing so, some predictor coefficients would be shrank and some others might be set to 0. We can attain good interpretation and prediction accuracy by using lasso method. Secondly, I provide some basic proofs for lasso, which would be very helpful in understanding lasso. Additionally, some geometric graphs are also given and one example is illustrated.
Files	he.pdf