Date of Award
2017-01-01
Degree Name
Master of Science
Department
Mathematical Sciences
Advisor(s)
Xiaogang Su
Abstract
The maximally selected statistic approach in building tree models is shown to be a cause of variable selection bias. In this study we propose three methods to solve this problem in building regression trees with nominal predictor variables. Out of the three methods
proposed we explored only two in detail and defer one for further research. We developed an exact method to compute the p-value corresponding to the maximized splitting statistic in regression trees for nominal predictor variables with at most 10 distinct levels and a
method to estimate the best cutoff point as a parameter in a parametric nonlinear mixed-effect model in regression trees for nominal predictor variables with any number of distinct levels. The methods are shown to overcome the variable selection bias in an extensive
simulation study and in a real data example.
Language
en
Provenance
Received from ProQuest
Copyright Date
2017-08
File Size
63 pages
File Format
application/pdf
Rights Holder
Isaac Xoese Ocloo
Recommended Citation
Ocloo, Isaac Xoese, "Evaluating Binary Splits On Nominal Inputs" (2017). Open Access Theses & Dissertations. 514.
https://scholarworks.utep.edu/open_etd/514