EconPapers    
Economics at your fingertips  
 

Classification using sequential order statistics

Alexander Katzur () and Udo Kamps ()
Additional contact information
Alexander Katzur: RWTH Aachen University
Udo Kamps: RWTH Aachen University

Advances in Data Analysis and Classification, 2020, vol. 14, issue 1, No 10, 230 pages

Abstract: Abstract Whereas discrimination methods and their error probabilities were broadly investigated for common data distributions such as the multivariate normal or t-distributions, this paper considers the case when the recorded data are assumed to be observations from sequential order statistics. Random vectors of sequential order statistics describe, e.g., successive failures in a k-out-of-n system or in other coherent and load sharing systems allowing for changes of underlying lifetime distributions caused by component failures. Within this framework, the Bayesian two-class discrimination approach with known prior probabilities and class parameters is considered, and exact and asymptotic formulas for the error probabilities in terms of Erlang and hypoexponential distributions are derived. Since the Bayesian classifier is closely related to Kullback–Leibler’s information distance, this approach is extended by invoking other divergence measures such as Jeffreys and Rényi’s distance. While exact formulas for the misclassification rates of the resulting distance-based classifiers are not available, inequalities among the corresponding error probabilities are derived. The performance of the applied classifiers is illustrated by some simulation results.

Keywords: Classification; Exponential families; Hypoexponential distribution; Jeffrey’s divergence; Kullback–Leibler divergence; Matusita’s affinity; Sequential order statistics; 62H30; 62G30 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11634-019-00368-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:advdac:v:14:y:2020:i:1:d:10.1007_s11634-019-00368-5

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-019-00368-5

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:advdac:v:14:y:2020:i:1:d:10.1007_s11634-019-00368-5