Tree-based Regression for Interval-valued Data

Author

Chih-Ching Yeh, Utah State UniversityFollow

Date of Award

8-2017

Degree Type

Report

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

Committee Chair(s)

Yan Sun

Committee

Yan Sun

Committee

Adele Cutler

Committee

John Stevens

Abstract

Regression methods for interval-valued data have been increasingly studied in recent years. As most of the existing works focus on linear models, it is important to note that many problems in practice are nonlinear in nature and therefore development of nonlinear regression tools for intervalvalued data is crucial. In this project, we propose a tree-based regression method for interval-valued data, which is well applicable to both linear and nonlinear problems. Unlike linear regression models that usually require additional constraints to ensure positivity of the predicted interval length, the proposed method estimates the regression function in a nonparametric way, so the predicted length is naturally positive without any constraints. A simulation study is conducted that compares our method to popular existing regression models for interval-valued data under both linear and nonlinear settings. Furthermore, a real data example is presented where we apply our method to analyze price range data of the Dow Jones Industrial Average index and its component stocks.

Recommended Citation

Yeh, Chih-Ching, "Tree-based Regression for Interval-valued Data" (2017). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 1010.
https://digitalcommons.usu.edu/gradreports/1010