Analysis of hybrid censored competing risks data

Bhattacharya, Shrijita; Pradhan, Biswabrata; Kundu, Debasis

doi:10.1080/02331888.2013.800076

Cited by 20 publications

(7 citation statements)

References 22 publications

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“…We propose to use the Gibbs sampling technique to construct the 100(1 − α)% HPD credible interval of any function of λ 1 and λ 2 , say, g(λ 1 , λ 2 ). Shrijita et al [9] gave similar algorithm to find HPD credible interval of g(λ 1 , λ 2 ) and hence are omitted here.…”

Section: Credible Setmentioning

confidence: 99%

On generalized progressive hybrid censoring in presence of competing risks

Koley

Kundu

2017

Metrika

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The progressive Type-II hybrid censoring scheme introduced by Kundu and Joarder (Computational Statistics and Data Analysis, 2509-2528, 2006, has received some attention in the last few years. One major drawback of this censoring scheme is that very few observations (even no observation at all) may be observed at the end of the experiment. To overcome this problem, Cho, Sun and Lee (Statistical Methodology, 23, 18-34, 2015) recently introduced generalized progressive censoring which ensures to get a pre specified number of failures. In this paper we analyze generalized progressive censored data in presence of competing risks. For brevity we have considered only two competing causes of failures, and it is assumed that the lifetime of the competing causes follow one parameter exponential distributions with different scale parameters. We obtain the maximum likelihood estimators of the unknown parameters and also provide their exact distributions. Based on the exact distributions of the maximum likelihood estimators exact confidence intervals can be obtained. Asymptotic and bootstrap confidence intervals are also provided for comparison purposes. We further consider the Bayesian analysis of the unknown parameters under a very flexible Beta-Gamma prior. We provide the Bayes estimates and the associated credible intervals of the unknown parameters based on the above priors. We present extensive simulation results to see the effectiveness of the proposed method and finally one real data set is analyzed for illustrative purpose., if T * = Z k:m:n , P (T < Z k:m:n < Z m:m:n , D 1 = i) dz 1 . . . dz k .

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Section: Credible Setmentioning

confidence: 99%

On generalized progressive hybrid censoring in presence of competing risks

Koley

Kundu

2017

Metrika

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“…This 'Dirichlet-gamma' distribution has been used in low-dimensional cases as a prior distribution in Bayesian analysis, in the guise of a 'beta-gamma' distribution by, e.g., Bhattacharya et al [7] when d = 2 and by Peña & Gupta [21] when d = 3. It is a particular continuous multivariate Liouville distribution [13].…”

Section: Negative Binomial Sums and Pólya Sharesmentioning

confidence: 99%

“…As an illustration, the choices a = µ 2 /(σ 2 − µ), θ = µ/σ 2 are necessary for the prior mean µ and standard deviation σ to be matched by the prior on T . The choice of (α 1 , α 2 ) may be elicited in a familiar fashion with a prior mean and variance for the Beta distributed p. Furthermore, the plausibility of an assigned choice for (α 1 , α 2 ) can be further evaluated by the corresponding covariance or correlation, which are available from (6) and (7), assuming, of course, that one can elicit such quantities.…”

Section: Application: Karl Broman's Socksmentioning

confidence: 99%

Multivariate discrete distributions via sums and shares

Jones

Marchand

2019

Journal of Multivariate Analysis

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“…We note that the assumption of the common shape parameter for the Weibull distribution in the case of competing risks model is not uncommon in the literature. 17–20 In the case of masked data, the parameters of the model are obtained via an expectation–maximization (EM) algorithm. When there is no masking, the proposed model becomes competing risks model.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for masked interval data with Weibull distribution under simple step-stress test and tampered failure rate model

Azizi

Haghighi

Ghadiri

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this article, we consider the planning of simple step-stress life test in the presence of competing risks with the possibility of masked causes of failure and interval censoring. It is assumed that the competing risks have independent Weibull distribution with the common shape parameter, but different scale parameters and a tampered failure rate model is hold. Maximum likelihood estimates of the model parameters are obtained for both cases, with and without masking. An expectation-maximization algorithm is used to estimate maximum likelihood estimates based on the masked data. For the case without masking, the Fisher information matrix is derived, and the problem of choosing the optimal test plan is considered using variance-optimality as well as determinant-optimality criteria. Through Monte Carlo simulations, the precision of the estimates is assessed and the optimal test plans are compared. Finally, the results are illustrated with an example.

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Analysis of hybrid censored competing risks data

Cited by 20 publications

References 22 publications

On generalized progressive hybrid censoring in presence of competing risks

On generalized progressive hybrid censoring in presence of competing risks

Multivariate discrete distributions via sums and shares

Statistical inference for masked interval data with Weibull distribution under simple step-stress test and tampered failure rate model

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