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Abstract Details
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Activity Number:
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667
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #301059 |
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Title:
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Variable Selection in Large P, Small N Problems with Applications to Tailored Therapeutics
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Author(s):
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Wei-Yin Loh*+
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Companies:
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University of Wisconsin
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Address:
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Department of Statistics, Madison, WI, 53706, United States
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Keywords:
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classification and regression trees ;
nonparametric regression ;
machine learning ;
random forest
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Abstract:
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Classification and regression problems in which the number of predictor variables, p, is larger than the number of observations, n, are increasingly common due to rapid technological advances in data collection. Because traditional solutions usually require p to be less than n, new approaches to solving these problems are needed. Two methods that have been proposed are Random forest (Breiman, Machine Learning 2001) and EARTH (Doksum, Tang and Tsui, JASA 2008). This talk presents a method based on the GUIDE classification and regression tree algorithm (Loh, Statist. Sinica 2002; Ann. Appl. Statist. 2009). Its performance against Random forest and EARTH is evaluated with simulated and real data.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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