Competing risk models in reliability systems, a weibull distribution model with bayesian analysis approach

Iskandar, Ismed; Gondokaryono, Yudi Satria

doi:10.1088/1757-899x/114/1/012064

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Hazhiah, dkk [5] mengkaji proses pengestimasian parameter dari distribusi Weibull terhadap data tak tersensor dan data tersendor ttpe II dengan metode Bayes (distribusi prior yang digunakan yaitu distribusi Gamma). Ismed dan Yudi [8] menggunakan metode Bayes dalam membuat model sistim risiko daya tahan yang bersaing (computing risk systems) dengan data diasumsikan berdistribusi Weibull. Kemudian Ahmed [6] dan Thamrin, dkk, [9] mengestimasi parameter dari distribusi Weibull dengan metode Bayesian survival (menggunakan prior non informatif distribusi Gamma) dan metode maksimum likelihood tetapi kedua hasil kajian mereka saling bertolak belakang.…”

Section: Pendahuluanunclassified

Analisis Survival Untuk Parameter Skala Dari Distribusi Weibull Menggunakan Mle Dan Metode Bayesian

Yanuar

Wulandari

2021

BAREKENG: J. Il. Mat. & Ter.

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Modeling of survival data is necessary and important to do. Survival data is generally assumed to have a Weibull distribution. Bayesian approach has been implemented to estimate the parameter in such this survival analysis. This study purposes to compare the performance of the Maximum Likelihood and Bayesian using Invers Gamma as prior conjugate for estimating the survival function of scale parameter of Weibull distribution. The comparisons are made through simulation study. The best performance of both estimators is chosen based on the lowest value of absolute bias and the mean square error. Two different size samples are generated to illustrate the life time data which are used in this study. This study results that maximum likelihood is the best estimator compared to Bayes with Invers Gamma distribution as conjugate prior.

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Section: Pendahuluanunclassified

Analisis Survival Untuk Parameter Skala Dari Distribusi Weibull Menggunakan Mle Dan Metode Bayesian

Yanuar

Wulandari

2021

BAREKENG: J. Il. Mat. & Ter.

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“…Modeling competing risks survival data can be carried out using a semi-parametric, nonparametric or parametric survival models (see Fine and Gray (1999)). The parametric models are studied assuming that the competing risks follow different lifetime distributions such as exponential, Lognormal and Weibull (see Sarhan (2007), Cox (1959), Pascual (2010), Yáñez et al (2014), Iskandar and Gondokaryono (2016)). One of the advantages of using the parametric approaches rather non-parametric and semiparametric approaches is as follows: when the parametric model has been chosen correctly, it is possible to predict the event occurrence probability in future and have a clear picture of survival time and hazard function.…”

Section: Introductionmentioning

confidence: 99%

Regression Modeling of Competing Risks Survival Data in the Presence of Covariates Based on a Generalized Weibull Distribution: A Simulation Study

Moamer

Baghestani

Pourhoseingholi

2018

Pak.j.stat.oper.res.

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In survival analysis or medical studies each person can be exposed to more than one type of outcomes which occurrence of one of them prevents the other outcomes' occurrence; this situation is called the competing risks. Assessing the effect of covariates on the survival time (or failure time) is one of the purposes in competing risks analysis. In this paper, we study a competing risks model in the presence of covariates when the causes of failures follow generalized Weibull distributions. Covariates are entered to the model through the scale parameter of this distribution. Also in this study the competing risks are considered to be independent. Parameter estimation has been done by the maximum likelihood approach, in a real data set and a simulation study has shown the advantages of proposed model.

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Competing Risk Models in Reliability Systems, an Exponential Distribution Model with Gamma Prior Distribution, a Bayesian Analysis Approach

Iskandar

Oktaviandri

Wangsaputra

et al. 2019

Lecture Notes in Mechanical Engineering

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Competing risk models in reliability systems, a weibull distribution model with bayesian analysis approach

Cited by 4 publications

References 10 publications

Analisis Survival Untuk Parameter Skala Dari Distribusi Weibull Menggunakan Mle Dan Metode Bayesian

Analisis Survival Untuk Parameter Skala Dari Distribusi Weibull Menggunakan Mle Dan Metode Bayesian

Regression Modeling of Competing Risks Survival Data in the Presence of Covariates Based on a Generalized Weibull Distribution: A Simulation Study

Competing Risk Models in Reliability Systems, an Exponential Distribution Model with Gamma Prior Distribution, a Bayesian Analysis Approach

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