Award Date

1-1-2006

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematical Sciences

First Committee Member

Rohan J. Dalpatadu

Number of Pages

51

Abstract

In multiple linear regression (MLR) the parameter estimates based on the method of least squares are typically unsatisfactory if the predictors are highly correlated. Ridge regression proposes a remedy to multicollinearity problems by modifying the method of least squares to allow biased estimation of the regression coefficients. This is a procedure that involves adding a small positive constant to the diagonal elements of X'X before numerically inverting it. The ridge regression method typically yields biased estimates with smaller mean square error. This thesis deals with estimating the standard errors of ridge estimates using bootstrap resampling. With code developed in R several examples are provided.

Keywords

Bootstrap; Error; Estimates; Estimation; Ridge; Standard

Controlled Subject

Mathematics

File Format

pdf

File Size

1054.72 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

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Identifier

https://doi.org/10.25669/l41i-my4c


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