EconPapers    
Economics at your fingertips  
 

Confidence Sets for Statistical Classification

Wei Liu, Frank Bretz, Natchalee Srimaneekarn, Jianan Peng and Anthony J. Hayter
Additional contact information
Wei Liu: S3RI and School of Mathematics, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Frank Bretz: Novartis Pharma AG, 4002 Basel, Switzerland
Natchalee Srimaneekarn: S3RI and School of Mathematics, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Jianan Peng: Department of Mathematics and Statistics, Acadia University, Wolfville, NS B4P 2R6, Canada
Anthony J. Hayter: Department of Statistics and Operations Technology, University of Denver, Denver, CO 80208-8921, USA

Stats, 2019, vol. 2, issue 3, 1-15

Abstract: Classification has applications in a wide range of fields including medicine, engineering, computer science and social sciences among others. In statistical terms, classification is inference about the unknown parameters, i.e., the true classes of future objects. Hence, various standard statistical approaches can be used, such as point estimators, confidence sets and decision theoretic approaches. For example, a classifier that classifies a future object as belonging to only one of several known classes is a point estimator. The purpose of this paper is to propose a confidence-set-based classifier that classifies a future object into a single class only when there is enough evidence to warrant this, and into several classes otherwise. By allowing classification of an object into possibly more than one class, this classifier guarantees a pre-specified proportion of correct classification among all future objects. An example is provided to illustrate the method, and a simulation study is included to highlight the desirable feature of the method.

Keywords: classification; confidence level; confidence set; coverage frequency; simultaneous tolerance intervals, statistical inference (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2571-905X/2/3/24/pdf (application/pdf)
https://www.mdpi.com/2571-905X/2/3/24/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:2:y:2019:i:3:p:24-346:d:244564

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jstats:v:2:y:2019:i:3:p:24-346:d:244564