Reliability estimation for Kumaraswamy distribution under block progressive type-II censoring

Kumari, Rani; Tripathi, Yogesh Mani; Wang, Liang; Sinha, Rajesh Kumar

doi:10.1080/02331888.2024.2301736

Cited by 3 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [2,3] estimated R(t) under type-I and type-II censoring, while for estimating 𝒫 , they used the complete sample case. This strategy's exceptional benefits have attracted extensive academic research, as evidenced by studies such as [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

New and Efficient Estimators of Reliability Characteristics for a Family of Lifetime Distributions under Progressive Censoring

Ahmed,

Belaghi,

Hussein

et al. 2024

Mathematics

View full text Add to dashboard Cite

Estimation of reliability and stress–strength parameters is important in the manufacturing industry. In this paper, we develop shrinkage-type estimators for the reliability and stress–strength parameters based on progressively censored data from a rich class of distributions. These new estimators improve the performance of the commonly used Maximum Likelihood Estimators (MLEs) by reducing their mean squared errors. We provide analytical asymptotic and bootstrap confidence intervals for the targeted parameters. Through a detailed simulation study, we demonstrate that the new estimators have better performance than the MLEs. Finally, we illustrate the application of the new methods to two industrial data sets, showcasing their practical relevance and effectiveness.

show abstract

Section: Introductionmentioning

confidence: 99%

New and Efficient Estimators of Reliability Characteristics for a Family of Lifetime Distributions under Progressive Censoring

Ahmed,

Belaghi,

Hussein

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

Reliability inference for a family of inverted exponentiated distributions under block progressive Type II censoring

Kumari,

Tripathi,

Wang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

In this article, estimation of survival characteristics is discussed when life tests are performed against different test facilities using block progressive Type-II censoring. Under the assumption that the lifetimes of test units follow a family of inverted exponentiated distributions, estimates are derived under classical and hierarchical Bayesian frameworks when the differences in different test facilities are taken into consideration. Maximum likelihood estimators are obtained and their existence and uniqueness properties are also established. In addition, Bayes estimators are provided under a hierarchical framework, and a hybrid Metropolis-Hastings sampling procedure is proposed for complex posterior computation. The performance of all the proposed approaches is compared via extensive simulation studies and two real-life data are analyzed for illustration. From numerical results we observe that the hierarchical approach provides relatively better estimates for model parameters.

show abstract

Analysis of Block Adaptive Type-II Progressive Hybrid Censoring with Weibull Distribution

Singh,

Tripathi,

Wang

et al. 2024

Mathematics

View full text Add to dashboard Cite

The estimation of unknown model parameters and reliability characteristics is considered under a block adaptive progressive hybrid censoring scheme, where data are observed from a Weibull model. This censoring scheme enhances experimental efficiency by conducting experiments across different testing facilities. Point and interval estimates for parameters and reliability assessments are derived using both classical and Bayesian approaches. The existence and uniqueness of maximum likelihood estimates are established. Consequently, reliability performance and differences across different testing facilities are analyzed. In addition, a Metropolis–Hastings sampling algorithm is developed to approximate complex posterior computations. Approximate confidence intervals and highest posterior density credible intervals are obtained for the parametric functions. The performance of all estimators is evaluated through an extensive simulation study, and observations are discussed. A cancer dataset is analyzed to illustrate the findings under the block adaptive censoring scheme.

show abstract

Reliability estimation for Kumaraswamy distribution under block progressive type-II censoring

Cited by 3 publications

References 29 publications

New and Efficient Estimators of Reliability Characteristics for a Family of Lifetime Distributions under Progressive Censoring

New and Efficient Estimators of Reliability Characteristics for a Family of Lifetime Distributions under Progressive Censoring

Reliability inference for a family of inverted exponentiated distributions under block progressive Type II censoring

Analysis of Block Adaptive Type-II Progressive Hybrid Censoring with Weibull Distribution

Contact Info

Product

Resources

About