Statistical inference of constant-stress partially accelerated life tests under type II generalized hybrid censored data from Burr III distribution

Abstract Accelerated life tests (ALTs) involve subjecting units to more extreme conditions than normal to reduce the duration of testing. These tests, whether fully accelerated or partially accelerated, are crucial in life testing research as they help save both time and money. When results from ALT cannot be extrapolated to normal circumstances, partial ALTs are performed. This study introduces a constant-stress partial ALT, which relies on a generalized Type-II hybrid censoring scheme and assumes that the lifetimes of units under the specified conditions follow the Burr III distribution. The estimates of parameters and accelerated factor of the Burr III distribution are obtained under normal use settings by applying the maximum likelihood and Bayesian techniques. Bayesian estimators are generated using symmetric and asymmetric loss functions through the Monte Carlo Markov Chain approach. Additionally, credible intervals and asymptotic confidence intervals are constructed. Simulation research is carried out using different censoring techniques and sample sizes in order to compare the suggested methodologies. Next, two data sets are analyzed to show the value of the proposed approaches. The study’s conclusion contains a summary of its main conclusions.

Authors

Institutions

Publication Details

Journal
Open Physics
Published
2025-01-01
DOI
https://doi.org/10.1515/phys-2025-0161
Citations
2
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
5.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Statistical inference of constant-stress partially accelerated life tests under type II generalized hybrid censored data from Burr III distribution

Ehab Mohamed Almetwally, Mohamed Kayid, Mohammed Elgarhy, Amal Soliman Hassan et al.
2 citations
Open Physics
Statistical Distribution Estimation and Applications
5.26
article

Statistical inference of constant-stress partially accelerated life tests under type II generalized hybrid censored data from Burr III distribution

Ehab Mohamed Almetwally, Mohamed Kayid, Mohammed Elgarhy, Amal Soliman Hassan, Najwan Alsadat, Oluwafemi Samson Balogun
article en
2 citations

Abstract

Abstract Accelerated life tests (ALTs) involve subjecting units to more extreme conditions than normal to reduce the duration of testing. These tests, whether fully accelerated or partially accelerated, are crucial in life testing research as they help save both time and money. When results from ALT cannot be extrapolated to normal circumstances, partial ALTs are performed. This study introduces a constant-stress partial ALT, which relies on a generalized Type-II hybrid censoring scheme and assumes that the lifetimes of units under the specified conditions follow the Burr III distribution. The estimates of parameters and accelerated factor of the Burr III distribution are obtained under normal use settings by applying the maximum likelihood and Bayesian techniques. Bayesian estimators are generated using symmetric and asymmetric loss functions through the Monte Carlo Markov Chain approach. Additionally, credible intervals and asymptotic confidence intervals are constructed. Simulation research is carried out using different censoring techniques and sample sizes in order to compare the suggested methodologies. Next, two data sets are analyzed to show the value of the proposed approaches. The study’s conclusion contains a summary of its main conclusions.

Open PhysicsVol. 23(1)
Cairo University (EG), University of Eastern Finland (FI), King Saud University (SA), High Institute for Management Sciences (EG), University of Business and Technology (SA), Delta University for Science and Technology (EG)
Openalex Percentile: Top 5%
Statistical Distribution Estimation and Applications
5.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.