Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis.
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Contributors
- Cham, Hei Ning (Author)
- Tein, Jenn-Yun (Thesis advisor)
- Enders, Stephen G (Thesis advisor)
- Enders, Craig K. (Committee member)
- Mackinnon, David P (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2013
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Note
- Partial requirement for: Ph. D., Arizona State University, 2013Note typethesis
- Includes bibliographical references (p. 77-81)Note typebibliography
- Field of study: Psychology
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Statement of Responsibility
by Hei Ning Cham