A common multivariate statistical problem is the prediction of two or more response variables using two or more predictor variables. The simplest model for this situation is the multivariate linear regression model. The standard least squares estimation for this model involves regressing each response variable separately on all the predictor variables. Breiman and Friedman found a way to take advantage of correlations among the response variables to increase the predictive accuracy for each of the response variables with an algorithm they called Curds and Whey. In this report, I describe an implementation of the Curds and Whey algorithm in the R language and environment for statistical computing, apply the algorithm to some simulated and real data sets, and discuss the R package I developed for Curds and Whey.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-3183 |
| Date | 01 May 2014 |
| Creators | Kidd, John |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). |
Page generated in 0.0016 seconds