Modeling Heterogeneous Survival Data Using Mixture of Extended Exponential-Geometric Distributions

Erişoğlu, Ülkü; Erol, Hamza

doi:10.1080/03610918.2010.524335

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Example 1: Fatigue lives dataset consists of 25 specimens of 2 different types. This data set was first discussed by Ling and Pan (1998) and later discussed by Erişoglu and Erol (2010).…”

Section: Applicationmentioning

confidence: 99%

L-Moments Estimations for Mixture of Weibull Distributions

̆glu¹,

̆glu²

2021

Journal of Data Science

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Mixture of Weibull distributions has wide application in modeling of heterogeneous data sets. The parameter estimation is one of the most important problems related to mixture of Weibull distributions. In this paper, we propose a L-moment estimation method for mixture of two Weibull distributions. The proposed method is compared with maximum likelihood estimation (MLE) method according to the bias, the mean absolute error, the mean total error and completion time of the algorithm (time) by simulation study. Also, applications to real data sets are given to show the flexibility and potentiality of the proposed estimation method. The comparison shows that, the proposed method is better than MLE method.

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“…Example 1: Fatigue lives dataset consists of 25 specimens of 2 different types. This data set was first discussed by Ling and Pan (1998) and later discussed by Erişoglu and Erol (2010).…”

Section: Applicationmentioning

confidence: 99%

L-Moments Estimations for Mixture of Weibull Distributions

̆glu¹,

̆glu²

2021

Journal of Data Science

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“…The shape and scale parameters 2  and 2  of the Weibull distribution in the proposed model are obtained by solving the equations (22) and (23) respectively [9,12,16,17].…”

Section: Weibull Distributionmentioning

confidence: 99%

“…Two components survival mixture models of Gamma-Gamma, Weibul-Weibull and Lognormal-lognormal were proposed to analyse survival data. Model selection method was used to select the model which better represents the real data [9]. A survival mixture of mixed distribution was employed for analyzing heterogeneous survival data.…”

Section: Introductionmentioning

confidence: 99%

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Mixture Model of the Exponential, Gamma and Weibull Distributions to Analyse Heterogeneous Survival Data

Mohammed¹,

Yatim²,

Ismail³

2015

JSRR

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Aims: In this study a survival mixture model of three components is considered to analyse survival data of heterogeneous nature. The survival mixture model is of the Exponential, Gamma and Weibull distributions. Methodology: The proposed model was investigated and the Maximum Likelihood (ML) estimators of the parameters of the model were evaluated by the application of the Expectation Maximization Algorithm (EM). Graphs, log likelihood (LL) and the Akaike Information Criterion (AIC) were used to compare the proposed model with the pure classical parametric survival models corresponding to each component using real survival data. The model was compared with the survival mixture models corresponding to each component. Results: The graphs, LL and AIC values showed that the proposed model fits the real data better than the pure classical survival models corresponding to each component. Also the proposed model fits the real data better than the survival mixture models corresponding to each component. Conclusion: The proposed model showed that survival mixture models are flexible and maintain the features of the pure classical survival model and are better option for modelling heterogeneous survival data.

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“…Also heterogeneous survival time data were modeled by a mixture model of Gamma-Gamma, a mixture of Lognormal-Lognormal and a mixture of the Weibull-Weibull distributions, where the best fit model to real survival time data was investigated [6]. A mixture model of mixed distributions was proposed to model heterogeneous survival time data, where a two component mixture model of the Extended Exponential-Geometric (EEG) distribution was used [7]. In an approach to employ different distributions, a mixture of two different distributions Exponential-Gamma, Exponential-Weibull and Gamma-Weibull were used to model heterogeneous survival data [8].…”

Section: Introductionmentioning

confidence: 99%