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Alternative Methods via Random Forest to Identify Interactions in a General Framework and Variable Importance in the Context of Value-Added Models


Abstract This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’ test scores as outcome variables and teachers’ contributions as random effects to ascribe changes in student performance to the teachers who have taught them. The VAMs teacher score is the empirical best linear unbiased predictor (EBLUP). This approach is limited by the adequacy of the assumed model specification with respect to the unknown underlying model. In that re... (more)
Created Date 2013
Contributor Valdivia, Arturo (Author) / Eubank, Randall (Advisor) / Young, Dennis (Committee member) / Reiser, Mark (Committee member) / Kao, Ming-Hung (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Subject Statistics / Data Mining / Interactions / Random Forest / Statistical Learning / Value Added Models / Variable Importance
Type Doctoral Dissertation
Extent 209 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Statistics 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis