This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:

320

Type:

Invited

Date/Time:

Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM

Sponsor:

IMS

Abstract  #306014 
Title:

PValues for Classification in HighDimensional Settings

Author(s):

Niki Zumbrunnen*+ and Lutz Dümbgen

Companies:

University of Bern and University of Bern

Address:

Alpeneggstrasse 22, Bern, International, 3012, Switzerland

Keywords:

nonparametric ;
regularization ;
datadriven tuning parameters ;
confidence region

Abstract:

Let (X,Y) be a random variable consisting of an observed feature vector X and an unobserved class label Y=1,2,...,L with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X,Y). Instead of providing point predictors (classifiers) for Y, we construct for each b=1,2,...,L a pvalue pi_b(X,D) for the null hypothesis that Y=b, treating Y temporarily as a fixed parameter, i.e. we construct a prediction region for Y with a certain confidence. Any reasonable classifier can be modified to yield nonparametric pvalues. For classifiers and pvalues involving tuning parameters, we propose datadriven choices. One example are nearest neighbor classifiers. A second example is multicategory logistic regression , where we use regularization terms to deal with highdimensional feature vectors X.

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