Optimal precedence tests under single and double-sampling framework

Niladri Chakraborty*, Narayanaswamy Balakrishnan, Maxim Finkelstein

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The precedence test is a nonparametric, two-sample test for stochastic comparison of lifetime data. The power of the precedence test increases with increasing sample size. However, the power curve of the precedence test follows a concave pattern that says the rate of increase in power decreases with increasing sample size. In this article, we intend to find the optimal sample size for the precedence test under the single and double-sampling frameworks. A genetic algorithm is used to find the optimal sample size.

Original languageEnglish
Article number115805
Number of pages13
JournalJournal of Computational and Applied Mathematics
Volume445
Early online date19 Feb 2024
DOIs
Publication statusPublished - 1 Aug 2024

Keywords

  • genetic algorithm
  • lifetime data
  • nonparametric test
  • power
  • precedence test

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