Master of Science (MS)
First Committee Member
Rohan J. Dalpatadu
Number of Pages
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.
Bootstrap; Error; Estimates; Estimation; Ridge; Standard
University of Nevada, Las Vegas
If you are the rightful copyright holder of this dissertation or thesis and wish to have the full text removed from Digital Scholarship@UNLV, please submit a request to firstname.lastname@example.org and include clear identification of the work, preferably with URL.
Capur, Mira, "Bootstrap estimation of standard error of ridge estimates" (2006). UNLV Retrospective Theses & Dissertations. 2044.