Date of Award:

5-2014

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Mathematics and Statistics

Committee Chair(s)

Richard Cutler

Committee

Richard Cutler

Committee

Adele Cutler

Committee

Christopher Corcoran

Abstract

A common statistical problem is trying to predict two or more variables using a set of predictor variables. The simplest model for this situation is called multivariate linear regression. This method uses each set of predictor variables to predict each of the response variables separately. This approach seems counter-intuitive as any possible relationship between the variables being predicted is ignored.

Breiman and Friedman found a way to take advantage of relationships among the response variables to increase the accuracy of the predictions for each of the predicted variables with an algorithm they called Curds and
Whey. It uses other statistical techniques to extract additional information from the variables being predicted to improve predictions on those same variables.

In this report, I describe an implementation of the Curds and Whey algorithm in a statistical software package called R, apply the algorithm to some simulated and real data sets, and discuss the R software package I developed for the Curds and Whey algorithm.

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173015ba90c56e942663a3bf562fd53e

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