Optimal precedence tests under single and double-sampling framework

Chakraborty, Niladri and Balakrishnan, Narayanaswamy and Finkelstein, Maxim (2024) Optimal precedence tests under single and double-sampling framework. Journal of Computational and Applied Mathematics, 445. 115805. ISSN 0377-0427 (https://doi.org/10.1016/j.cam.2024.115805)

[thumbnail of Chakraborty-etal-JCAM-2024-Optimal-precedence-tests-under-single-and-double-sampling-framework] Text. Filename: Chakraborty-etal-JCAM-2024-Optimal-precedence-tests-under-single-and-double-sampling-framework.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 19 February 2025.
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (1MB) | Request a copy


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.