JSM 2011 Online Program

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Abstract Details

Activity Number: 152
Type: Invited
Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #300263
Title: DD-Classifier: Nonparametric Classification Procedure Based on DD-Plot
Author(s): Jun Li*+ and Juan Antonio Cuesta-Albertos and Regina Y. Liu
Companies: University of California at Riverside and University of Cantabria and Rutgers University
Address: Department of Statistics, Riverside, CA, 92508,
Keywords: Classification ; data depth ; DD-plot ; DD-classifier ; nonparametric ; robustness
Abstract:

Using the DD-plot (depth-versus-depth plot), we introduce a new nonparametric classification algorithm and call it a DD-classifier. The algorithm is completely nonparametric, and requires no prior knowledge of the underlying distributions or of the form of the separating curve. Thus it can be applied to a wide range of classification problems. The algorithm is completely data driven and its classification outcome can be easily visualized on a two-dimensional plot regardless of the dimension of the data. Moreover, it is easy to implement since it bypasses the task of estimating underlying parameters such as means and scales, which is often required by the existing classification procedures. We study the asymptotic properties of the proposed DD-classifier and its misclassification rate. Specifically, we show that it is asymptotically equivalent to the Bayes rule under suitable conditions. The performance of the classifier is also examined by using simulated and real data sets. Overall, the proposed classifier performs well across a broad range of settings, and compares favorably with existing classifiers. Finally, it can also be robust against outliers or contamination.


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