Zumbrunnen, Niki; Dümbgen, Lutz (2017). pvclass: An R Package for p Values for Classification. Journal of statistical software, 78(4), pp. 119. UCLA Statistics 10.18637/jss.v078.i04

Text
v78i04.pdf  Published Version Available under License Creative Commons: Attribution (CCBY). Download (1MB)  Preview 
Let (X,Y) be a random variable consisting of an observed feature vector X and an unobserved class label Y ∈ {12,...,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 compute for each b ∈ {12,...,L} ap value πb (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. The advantages of this approach over more traditional ones are reviewed briefly. In principle, any reasonable classifier can be modified to yield nonparametric pvalues. We describe the R package pvclass which computes nonparametric p values for the potential class memberships of new observations as well as crossvalidated pvalues for the training data. Additionally, it provides graphical displays and quantitative analyses of the p values.
Item Type: 
Journal Article (Original Article) 

Division/Institute: 
08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science 
UniBE Contributor: 
Zumbrunnen, Niki and Dümbgen, Lutz 
Subjects: 
500 Science > 510 Mathematics 
ISSN: 
15487660 
Publisher: 
UCLA Statistics 
Language: 
English 
Submitter: 
Lutz Dümbgen 
Date Deposited: 
25 Jul 2017 09:45 
Last Modified: 
14 Mar 2021 02:19 
Publisher DOI: 
10.18637/jss.v078.i04 
BORIS DOI: 
10.7892/boris.101172 
URI: 
https://boris.unibe.ch/id/eprint/101172 