Bivariate Length-Biased Exponential Distribution under Progressive Type-II Censoring: Incorporating Random Removal and Applications to Industrial and Computer Science Data

In this paper, we address the analysis of bivariate lifetime data from a length-biased exponential distribution observed under Type II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial distribution. We derive the likelihood function...

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Vydáno v:Axioms Ročník 13; číslo 10; s. 664
Hlavní autoři: Fayomi, Aisha, Almetwally, Ehab M., Qura, Maha E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2024
Témata:
Asymptotic methods
Bayesian analysis
bayesian estimation
Bivariate analysis
bivariate length-biased exponential distribution
Censoring (Statistics)
Clinical trials
Computer science
Confidence intervals
Data analysis
Distribution (Probability theory)
Electronic data processing
Estimators
Experiments
Failure
Failure times
farlie-gumbel-morgenstern copula
Industrial applications
loss function
Mathematical research
maximum likelihood estimation
Methods
Monte Carlo simulation
Parameter estimation
Probability distribution functions
progressive type-II censoring
Random variables
Saudi Arabia
ISSN:2075-1680, 2075-1680
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Shrnutí:In this paper, we address the analysis of bivariate lifetime data from a length-biased exponential distribution observed under Type II progressive censoring with random removals, where the number of units removed at each failure time follows a binomial distribution. We derive the likelihood function for the progressive Type II censoring scheme with random removals and apply it to the bivariate length-biased exponential distribution. The parameters of the proposed model are estimated using both likelihood and Bayesian methods for point and interval estimators, including asymptotic confidence intervals and bootstrap confidence intervals. We also employ different loss functions to construct Bayesian estimators. Additionally, a simulation study is conducted to compare the performance of censoring schemes. The effectiveness of the proposed methodology is demonstrated through the analysis of two real datasets from the industrial and computer science domains, providing valuable insights for illustrative purposes.
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ISSN:2075-1680
2075-1680
DOI:10.3390/axioms13100664