Decision-Making for the Lifetime Performance Index

Basim S O Alsaedi; M M Abd El-Raouf; E H Hafez; Zahra Almaspoor; Osama Abdulaziz Alamri; Kamel Atallah Alanazi; Saima Khan Khosa

doi:10.1155/2021/3005067

Decision-Making for the Lifetime Performance Index

Comput Intell Neurosci. 2021 Jul 12:2021:3005067. doi: 10.1155/2021/3005067. eCollection 2021.

Authors

Basim S O Alsaedi¹, M M Abd El-Raouf², E H Hafez³, Zahra Almaspoor⁴, Osama Abdulaziz Alamri¹, Kamel Atallah Alanazi⁵, Saima Khan Khosa⁶

Affiliations

¹ Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia.
² Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandia, Egypt.
³ Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt.
⁴ Department of Statistics, Yazd University, P. O. Box 89175-741, Yazd, Iran.
⁵ University of Jeddah, Faculty of Science and Arts (Al Kamil), Department of Mathematics, Jeddah, Saudi Arabia.
⁶ Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.

Abstract

The purpose of this research is to develop a maximum likelihood estimator (MLE) for lifetime performance index C _L for the parameter of mixture Rayleigh-Half Normal distribution (RHN) under progressively type-II right-censored samples under the constraint of knowing the lower specification limit (L). Additionally, we suggest an asymptotic normal distribution for the MLE for C _L in order to construct a mechanism for evaluating products' lifespan efficiency. We have specified all the steps to carry out the test. Additionally, not only does hypothesis testing successfully assess the lifetime performance of items, but it also functions as a supplier selection criterion for the consumer. Finally, we have added two real data examples as illustration examples. These two applications are provided to demonstrate how the results can be applied.

MeSH terms

Likelihood Functions*