Classification using sequential order statistics

Alexander Katzur () and Udo Kamps ()
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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
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DOI: 10.1007/s11634-019-00368-5

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