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Activity Number: 652
Type: Contributed
Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #312676 View Presentation
Title: A Review on Combining Statistical Procedures in High-Dimensional Data Analysis
Author(s): Yanjia Yu*+
Companies: University of Minnesota, Twin Cities
Keywords: combining statistical procedures ; high dimensional data ; model selection
Abstract:

One of the main interests in statistical analysis is prediction. For the same data, if we use different statistical models we might get quite different prediction results. This is especially common in high dimensional data analysis, where there is high model selection uncertainty. To reduce the variance in prediction, the idea of combining statistical procedures is proposed. Combining is popular and useful, but there is not too much thorough and up-to-date review on this topic in high-dimensional data analysis. In this paper, we review many popular approaches in combining procedures: Bayesian model averaging, information criterion based weighting methods, Bagging, random forest, and ARM, to name a few. We compare those combining procedures in simulation studies and real data examples to evaluate their performance in high-dimensional data analysis. We also summarize the algorithms and coding resources for those popular combining procedures. This review is a helpful guide for statisticians who want to use combining procedure in high-dimensional data analysis.


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