Implementation and Application of the Curds and Whey Algorithm to Regression Problems
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 respon...
| Main Author: | |
|---|---|
| Format: | Others |
| Published: |
DigitalCommons@USU
2014
|
| Subjects: | |
| Online Access: | https://digitalcommons.usu.edu/etd/2167 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=3183&context=etd |
| Summary: | 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. |
|---|