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Title:Partially Bayesian Variable Selection in Classification Trees
Author(s):Noe, Douglas Alan
Doctoral Committee Chair(s):He, Xuming
Department / Program:Statistics
Discipline:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Statistics
Abstract:An algorithm that dynamically incorporates expert opinion in this way has two potential advantages, each improving with the quality of the expert. First, by de-emphasizing certain subsets of variables during the estimation process, unnecessary computational activity can be avoided. Second, by giving an expert's preferred variables priority, we reduce the chance that a spurious variable will appear in the model. Hence, our resulting models are potentially more interpretable and less unstable than those generated by purely data-driven algorithms. We examine these properties in both applied and simulated contexts, and discuss potential extensions of our partially Bayesian algorithm to ensemble trees and other classification and prediction methods.
Issue Date:2006
Type:Text
Language:English
Description:106 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
URI:http://hdl.handle.net/2142/87407
Other Identifier(s):(MiAaPQ)AAI3242952
Date Available in IDEALS:2015-09-28
Date Deposited:2006


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