Bayesian and maximum likelihood estimations of the inverse Weibull parameters under progressive type-II censoring

Sultan, Khalaf S.; Alsadat, Najwan; Kundu, Debasis

doi:10.1080/00949655.2013.788652

Cited by 67 publications

(36 citation statements)

References 17 publications

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“…Authors further considered real data set for each mechanism and established that the inverse Weibull distribution is more reliable as compared to the inverse Gaussian and the lognormal distributions. One may also refer to Jiang et al (2003), Maswadah (2003), Singh et al (2013), Kim et al (2014) and Sultan et al (2014) for some useful inferential results on the inverse Weibull distribution. Censoring is very common in reliability analysis.…”

Section: Introductionmentioning

confidence: 97%

Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring

Singh

Tripathi

2016

Stat Papers

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In this paper, we consider the problem of estimating unknown parameters of an inverse Weibull distribution when it is known that samples are progressive type-I interval censored. We propose an EM algorithm to obtain maximum likelihood estimates and mid point estimates. For comparison purpose Bayes estimates are also obtained under the square error loss function. A simulation study is conducted to access the performance of the proposed estimators and recommendations are made on the basis of simulation results. A real data set is also analyzed in detail for an illustration purpose. Finally, by making use of expected Fisher information matrix various inspection times and optimal censoring schemes are obtained.

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Section: Introductionmentioning

confidence: 97%

Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring

Singh

Tripathi

2016

Stat Papers

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“…Ghitany, Alqallaf, and Balakrishnan (2014) discussed estimation of the parameters of Gompertz distributions based on progressively Type-II censored samples. Sultan, Alsadat, and Kundu (2014) studied estimation for the inverse Weibull parameters under progressive Type-II censoring.…”

Section: Potdar and Shirke 325mentioning

confidence: 99%

Confidence intervals for the scaled half-logistic distribution under progressive Type-II censoring

Potdar¹,

Shirke

2017

J. Mod. App. Stat. Meth.

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Confidence interval construction for the scale parameter of the half-logistic distribution is considered using four different methods. The first two are based on the asymptotic distribution of the maximum likelihood estimator (MLE) and log-transformed MLE. The last two are based on pivotal quantity and generalized pivotal quantity, respectively. The MLE for the scale parameter is obtained using the expectation-maximization (EM) algorithm. Performances are compared with the confidence intervals proposed by Balakrishnan and Asgharzadeh via coverage probabilities, length, and coverage-to-length ratio. Simulation results support the efficacy of the proposed approach.

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“…(34) and (35) with the scale parameters obtained through moment matching first as in (33). This mixture model is a 3-parameter distribution with the parameter set as θ = {w 1 , λ, c}.…”

Section: Theorem 1 Given a Data Set Y The Pdf Of The Mixture Intermentioning

confidence: 99%

“…It is also specified by two parameters, a shape parameter c and a scale parameter b (or λ IW in some text). Given an IW random variable γ IW (b, c), its probability density function is defined as [33], [34] …”

Section: Candidate Distributions For the Interference Modelmentioning

confidence: 99%

Interference Modeling for Cellular Networks Under Beamforming Transmission

Elkotby¹,

Vu²

2017

IEEE Trans. Wireless Commun.

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We propose analytical models for the interference power distribution in a cellular system employing MIMO beamforming in rich and limited scattering environments, which capture non line-of-sight signal propagation in the microwave and mmWave bands, respectively. Two candidate models are considered:the Inverse Gaussian and the Inverse Weibull, both are two-parameter heavy tail distributions. We further propose a mixture of these two distributions as a model with three parameters. To estimate the parameters of these distributions, three approaches are used: moment matching, individual distribution maximum likelihood estimation (MLE), and mixture distribution MLE with a designed expectation maximization algorithm. We then introduce simple fitted functions for the mixture model parameters as polynomials of the channel path loss exponent and shadowing variance. To measure the goodness of these models, the information-theoretic metric relative entropy is used to capture the distance from the model distribution

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Bayesian and maximum likelihood estimations of the inverse Weibull parameters under progressive type-II censoring

Cited by 67 publications

References 17 publications

Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring

Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring

Confidence intervals for the scaled half-logistic distribution under progressive Type-II censoring

Interference Modeling for Cellular Networks Under Beamforming Transmission

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