A modified Weibull distribution

Lai, C. D.; Xie, Min; Murthy, D. N. P.

doi:10.1109/tr.2002.805788

Cited by 477 publications

(272 citation statements)

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“…Also when both 2   and 0   then MWD reduces to oneparameter Rayleigh distribution. The modified Weibull model was developed by Xie et al(2003) [1] this lifetime distribution is an important feature for reliability analysis.…”

Section: T T T T T E T F T Owmentioning

confidence: 99%

Comparison of four non-Bayesian methods to estimate the scale parameter for Modified Weibull distribution by using the Simulation

Hassan Hussein,

Ghdhaib Kalt

2018

J. Al-Qadisiyah Comp. Sci. Math.

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Abstract.,In this paper, four methods were obtained to estimate the scale parameter of Modified Weibull distribution using complete data, which are the Modified Moments Estimator (MME), the Maximum Likelihood Estimator (MLE), White Estimator (WE) and Least Squares Estimator (LSE), Monte Carlo simulation is used to compare these four estimators with respect to the Mean Square Error criteria (MSE), and the results on simulated samples of the comparison showed that for all the varying sample size in this study, and in all cases for the four methods The MLE method is best followed by the OLSE method then the WE method and the MME method .

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Section: T T T T T E T F T Owmentioning

confidence: 99%

Comparison of four non-Bayesian methods to estimate the scale parameter for Modified Weibull distribution by using the Simulation

Hassan Hussein,

Ghdhaib Kalt

2018

J. Al-Qadisiyah Comp. Sci. Math.

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“…The other fitted models are: the additive Weibull (AddW) [54], modified-Weibull (MW) [30], Sarhan-Zaindin modified Weibull (SZMW) [48] and beta-modified Weibull (BMW) [50]. Their associated densities are given by:…”

Section: Data Set 2: Device Failure Times Datamentioning

confidence: 99%

The Weibull-Power Function Distribution With Applications

Tahir¹,

Alizadeh²,

Mansoor³

et al. 2014

HJMS

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Recently, several attempts have been made to define new models that extend well-known distributions and at the same time provide great flexibility in modelling real data. We propose a new four-parameter model named the Weibull-power function (WPF) distribution which exhibits bathtub-shaped hazard rate. Some of its statistical properties are obtained including ordinary and incomplete moments, quantile and generating functions, Rényi and Shannon entropies, reliability and order statistics. The model parameters are estimated by the method of maximum likelihood. A bivariate extension is also proposed. The new distribution can be implemented easily using statistical software packages. We investigate the potential usefulness of the proposed model by means of two real data sets. In fact, the new model provides a better fit to these data than the additive Weibull, modified Weibull, Sarahan-Zaindin modified Weibull and beta-modified Weibull distributions, suggesting that it is a reasonable candidate for modeling survival data.

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“…Discrete Burr type II is introduced by Al-Huniti and Al-Dayian (2012), using the same approach. Nooghabi et al (2012) used the continuous modified Weibull distribution of Lai et al (2003) to propose a new modified version of discrete Weibull distribution (DMW). Bebbington et al (2012) introduced discrete additive Weibull distribution (DAddW) using the continuous additive Weibull distribution of Xie and Lai (1996).…”

Section: Introductionmentioning

confidence: 99%

A two-parameter discrete distribution with a bathtub hazard shape

Sarhan

2017

CSAM

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This paper introduces a two-parameter discrete distribution based on a continuous two-parameter bathtub distribution. It is the only two-parameter discrete distribution that shows a bathtub-shaped hazard function. Some statistical properties of the distribution are discussed. Three different methods are used to estimate its two unknown parameters. The point estimators of the parameters have no closed form. The bootstrap method is used to estimate the distributions of these point estimators. Different approximations of the interval estimations for the two-parameters are discussed. Real data sets are analyzed to show how this distribution works in practice. A simulation study is performed to investigate the properties of the estimations obtained and compare their performances.

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A modified Weibull distribution

Cited by 477 publications

References 10 publications

Comparison of four non-Bayesian methods to estimate the scale parameter for Modified Weibull distribution by using the Simulation

Comparison of four non-Bayesian methods to estimate the scale parameter for Modified Weibull distribution by using the Simulation

The Weibull-Power Function Distribution With Applications

A two-parameter discrete distribution with a bathtub hazard shape

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