Inference for a general family of inverted exponentiated distributions under unified hybrid censoring with partially observed competing risks data

Dutta, Subhankar; Ng, Hon Keung Tony; Kayal, Suchandan

doi:10.1016/j.cam.2022.114934

Cited by 15 publications

(10 citation statements)

References 47 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 8 For constructing (1 − 𝛾)% credible interval with arbitrary 0 < 𝛾 < 1, we first arrange remaining 𝑁 𝑀 samples of 𝑔(𝜂) in ascending order as 𝑔 [1] < 𝑔 [2] < ⋯ < 𝑔 [𝑁−𝑀] .…”

Section: Bayesian Inference With Nonorder Restrictionmentioning

confidence: 99%

“…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%

“…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%

See 2 more Smart Citations

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: Bayesian Inference With Nonorder Restrictionmentioning

confidence: 99%

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

show abstract

“…The second direction involves the use of competing risks data under UHCS to estimate the parameters

and θ . For more details on recent studies using partially observed competing risks data, see [21] , [22] , [23] , [24] , [25] , [26] , and [27] .…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for Nadarajah-Haghighi distribution under unified hybrid censored competing risks data

Abushal

2024

Heliyon

View full text Add to dashboard Cite

“…Jamal et al [9] studied the type II general inverse exponential family of distributions. Dutta et al [10] obtained useful estimation results for a general family of inverted exponentiated distributions under unified hybrid censoring, with partially observed competing risk data. Hashem et al [11] estimated the reliability of the IER distribution by using the Bayesian method under generalized progressive hybrid censored data, among others.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Estimation Methods for Reliability Function for Family of Inverse Exponentiated Distributions under New Loss Function

Kumari,

Tripathi,

Sinha

et al. 2023

Axioms

View full text Add to dashboard Cite

In this paper, different estimation is discussed for a general family of inverse exponentiated distributions. Under the classical perspective, maximum likelihood and uniformly minimum variance unbiased are proposed for the model parameters. Based on informative and non-informative priors, various Bayes estimators of the shape parameter and reliability function are derived under different losses, including general entropy, squared-log error, and weighted squared-error loss functions as well as another new loss function. The behavior of the proposed estimators is evaluated through extensive simulation studies. Finally, two real-life datasets are analyzed from an illustration perspective.

show abstract

Inference for a general family of inverted exponentiated distributions under unified hybrid censoring with partially observed competing risks data

Cited by 15 publications

References 47 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

Statistical inference for Nadarajah-Haghighi distribution under unified hybrid censored competing risks data

Comparison of Estimation Methods for Reliability Function for Family of Inverse Exponentiated Distributions under New Loss Function

Contact Info

Product

Resources

About