Approximate Bayesian analysis of doubly censored samples from mixture of two Weibull distributions

Feroze, Navid; Aslam, Muhammad

doi:10.1080/03610926.2018.1473430

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Mixture models have been effectively used in many areas such as industrial engineering (Ali et al [1]), biology (Bhattacharya [2]), social sciences (Harris [3]), economics (Jedidi et al [4]), and reliability (Sultan et al [5]). For more detail about the finite mixture models, see Everitt [6], Ali [7], Feroze and Aslam [8], Zhang and Huang [9], Fundi et al [10], Tripathi et al [11], Noor et al [12], and Feroze and Aslam [13].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

Noor

Masood

Zaman

et al. 2021

Mathematical Problems in Engineering

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Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors.

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Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

Noor

Masood

Zaman

et al. 2021

Mathematical Problems in Engineering

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show abstract

“…The maximum likelihood and Bayesian estimators of the parameters of a heterogeneous population represented by a finite mixture of two Pareto distributions were discussed in [ 22 ]. Feroze and Aslam [ 23 ] introduced the Bayesian approach for estimating the parameters of the two-component mixture of Weibull distribution under doubly censored samples. Feroze and Aslam [ 24 ] considered the Bayesian analysis of the three-component mixture of the Rayleigh distribution under doubly censored samples.…”

Section: Introductionmentioning

confidence: 99%

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

Zhou

Shi

2020

Entropy

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In this paper, the evidential estimation method for the parameters of the mixed exponential distribution is considered when a sample is obtained from Type-II progressively censored data. Different from the traditional statistical inference methods for censored data from mixture models, here we consider a very general form where there is some uncertain information about the sub-class labels of units. The partially specified label information, as well as the censored data are represented in a united frame by mass functions within the theory of belief functions. Following that, the evidential likelihood function is derived based on the completely observed failures and the uncertain information included in the data. Then, the optimization method using the evidential expectation maximization algorithm (E2M) is introduced. A general form of the maximal likelihood estimates (MLEs) in the sense of the evidential likelihood, named maximal evidential likelihood estimates (MELEs), can be obtained. Finally, some Monte Carlo simulations are conducted. The results show that the proposed estimation method can incorporate more information than traditional EM algorithms, and this confirms the interest in using uncertain labels for the censored data from finite mixture models.

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Approximate Bayesian analysis of doubly censored samples from mixture of two Weibull distributions

Cited by 2 publications

References 18 publications

Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

Evidential Estimation of an Uncertain Mixed Exponential Distribution under Progressive Censoring

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