Parameter estimation of Lindley step stress model with independent competing risk under type 1 censoring

Varghese, A Sharon; Vaidyanathan, V. S.

doi:10.1080/03610926.2019.1584317

Cited by 6 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For traditional analysis of competing risk data, it is commonly assumed that the causes of failure are statistically independence for concision (e.g., Refs. [1][2][3][4][5][6]. However, practical failure factors may affect each other due to complex operating mechanism in applications, and such an independent assumption may be unsuitable in analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inference for dependent complementary competing risks model from an inverted Kumaraswamy distribution under ranked set sampling

Wang,

Lio,

Tripathi

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

Ranked set sampling (RSS) has been proved to be an efficient sampling design for parametric and nonparametric inference. This paper explores inference for a maximum RSS procedure with unequal samples (MRSSU) with multiple dependent failure causes. When the lifetimes of units are characterized by a proposed complementary competing risks model, classical likelihood and Bayesian approaches are discussed for parameters and reliability estimation. Maximum likelihood estimators of model parameters and associated existence and uniqueness are established, and approximate confidence intervals are constructed using asymptotic theory and delta methods. With respect to general flexible priors, Bayesian estimates of interested quantities are also performed and a Monte Carlo sampling algorithm is proposed for complex posterior computation. Additionally, when extra historical information between the competing risks parameters is available, likelihood and Bayesian estimation are also studied under an order restriction case. Different methods are compared based on extensive simulation studies, and another real data example is demonstrated for application propose.

show abstract

Section: Introductionmentioning

confidence: 99%

“…This phenomenon refers to competing risks model in literature. For traditional analysis of competing risk data, it is commonly assumed that the causes of failure are statistically independence for concision (e.g., Refs 1–6…”

Section: Introductionmentioning

confidence: 99%

Inference for dependent complementary competing risks model from an inverted Kumaraswamy distribution under ranked set sampling

Wang,

Lio,

Tripathi

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Inference for competing risks data has attracted wide attention and been discussed by many authors. See, some recent works of Rafiee et al [1], Balakrishnan et al [2], Varghese and Vaidyanatha [3], Koley et al [4], among others. In traditional analysis, failure causes are commonly treated as independent, but such assumption sometimes may be improper due to practical complexity.…”

Section: Introductionmentioning

confidence: 99%

Reliability Estimation for Dependent Left-Truncated and Right-Censored Competing Risks Data with Illustrations

2022

View full text Add to dashboard Cite

In this paper, a competing risks model with dependent causes of failure is considered under left-truncated and right-censoring scenario. When the dependent failure causes follow a Marshall–Olkin bivariate exponential distribution, estimation of model parameters and reliability indices are proposed from classic and Bayesian approaches, respectively. Maximum likelihood estimators and approximate confidence intervals are constructed, and conventional Bayesian point and interval estimations are discussed as well. In addition, E-Bayesian estimators are proposed and their asymptotic behaviors have been investigated. Further, another objective-Bayesian analysis is also proposed when a noninformative probability matching prior is used. Finally, extensive simulation studies are carried out to investigate the performance of different methods. Two real data examples are presented to illustrate the applicability.

show abstract

“…The competing failure model plays an important role and the competing failure data are analyzed by Cox [ 10 ] and David et al [ 11 ]. In S-SALT, some researchers have discussed the competing failure model, such as Balakrishnan et al [ 12 ], Beltrami [ 13 , 14 ], Shi and Liu [ 15 ], Srivastava et al [ 16 ], Xu et al [ 17 ], Zhang et al [ 18 , 19 ], Ganguly et al [ 20 ], Han et al [ 21 , 22 , 23 ], Varhgese et al [ 24 ], Liu et al [ 25 ], Abu-Zinadah et al [ 26 ] and Aljohaniet al [ 27 ]. The maximum likelihood estimation (MLE) and the Bayesian estimation (BE) based on the different loss function (LF) are the common inference for analyzing statistical data.…”

Section: Introductionmentioning

confidence: 99%

E-Bayesian and H-Bayesian Inferences for a Simple Step-Stress Model with Competing Failure Model under Progressively Type-II Censoring

Wang

Yan

Chen

2022

Entropy

View full text Add to dashboard Cite

In this paper, we discuss the statistical analysis of a simple step-stress accelerated competing failure model under progressively Type-II censoring. It is assumed that there is more than one cause of failure, and the lifetime of the experimental units at each stress level follows exponential distribution. The distribution functions under different stress levels are connected through the cumulative exposure model. The maximum likelihood, Bayesian, Expected Bayesian, and Hierarchical Bayesian estimations of the model parameters are derived based on the different loss function. Based on Monte Carlo Simulations. We also get the average length and the coverage probability of the 95% confidence intervals and highest posterior density credible intervals of the parameters. From the numerical studies, it can be seen that the proposed Expected Bayesian estimations and Hierarchical Bayesian estimations have better performance in terms of the average estimates and mean squared errors, respectively. Finally, the methods of statistical inference discussed here are illustrated with a numerical example.

show abstract

Parameter estimation of Lindley step stress model with independent competing risk under type 1 censoring

Cited by 6 publications

References 16 publications

Inference for dependent complementary competing risks model from an inverted Kumaraswamy distribution under ranked set sampling

Inference for dependent complementary competing risks model from an inverted Kumaraswamy distribution under ranked set sampling

Reliability Estimation for Dependent Left-Truncated and Right-Censored Competing Risks Data with Illustrations

E-Bayesian and H-Bayesian Inferences for a Simple Step-Stress Model with Competing Failure Model under Progressively Type-II Censoring

Contact Info

Product

Resources

About