• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.479-493
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• 2012

The inverse Weibull distribution(IWD) is a complementary Weibull distribution and plays an important role in many application areas. In this paper, we develop a Bayesian estimator in the context of record statistics values from the exponentiated inverse Weibull distribution(EIWD). We obtained Bayesian estimators through the squared error loss function (quadratic loss) and LINEX loss function. This is done with respect to the conjugate priors for shape and scale parameters. The results may be of interest especially when only record values are stored.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.859-870
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• 2009

We obtain a skewed double Weibull distribution by a double Weibull distribution, and evaluate its coefficient of skewness. And we obtain the approximate maximum likelihood estimator(AML) and moment estimator of skew parameter in the skewed double Weibull distribution, and hence compare simulated mean squared errors(MSE) of those estimators. We compare simulated MSE of two proposed reliability estimators in two independent skewed double Weibull distributions each with different skew parameters. Finally we introduce a skewed double Weibull distribution generated by a uniform kernel.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.147-160
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• 2014

In this paper, we introduce the exponentiated Weibull-geometric (EWG) distribution which generalizes two-parameter exponentiated Weibull (EW) distribution introduced by Mudholkar et al. (1995). This proposed distribution is obtained by compounding the exponentiated Weibull with geometric distribution. We derive its cumulative distribution function (CDF), hazard function and the density of the order statistics and calculate expressions for its moments and the moments of the order statistics. The hazard function of the EWG distribution can be decreasing, increasing or bathtub-shaped among others. Also, we give expressions for the Renyi and Shannon entropies. The maximum likelihood estimation is obtained by using EM-algorithm (Dempster et al., 1977; McLachlan and Krishnan, 1997). We can obtain the Bayesian estimation by using Gibbs sampler with Metropolis-Hastings algorithm. Also, we give application with real data set to show the flexibility of the EWG distribution. Finally, summary and discussion are mentioned.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.211-223
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• 1997

We introduce the linear estimators based on order statistics in the three-parameter Weibull distribution and compare the small sample performances of proposed linear estimators in the three- parameter Weibull distribution with an unidentified outlier.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.593-603
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• 2004

We consider the problem of estimating the scale parameter of the Weibull distribution based on multiply Type-II censored samples. We propose two estimators by using the approximate maximum likelihood estimation method for Weibull and extreme value distributions. The proposed estimators are compared in the sense of the mean squared error.

• Communications for Statistical Applications and Methods
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• v.23 no.5
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• pp.399-409
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• 2016

Many of studies have suggested the modifications on Weibull distribution to model the non-monotone hazards. In this paper, we combine two cumulative hazard functions and propose a new modified Weibull distribution function. The newly suggested distribution will be named as a new flexible Weibull distribution. Corresponding hazard function of the proposed distribution shows flexible (monotone or non-monotone) shapes. We study the characteristics of the proposed distribution that includes ageing behavior, moment, and order statistic. We also discuss an estimation method for its parameters. The performance of the proposed distribution is compared with existing modified Weibull distributions using various types of hazard functions. We also use real data example to illustrate the efficiency of the proposed distribution.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.611-622
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• 2014

The inverse Weibull distribution (IWD) is the complementary Weibull distribution and plays an important role in many application areas. In Bayesian analysis, Soland's method can be considered to avoid computational complexities. One limitation of this approach is that parameters of interest are restricted to a finite number of values. This paper introduce nonparametric Bayesian estimator in the context of record statistics values from the exponentiated inverse Weibull distribution (EIWD). In stead of Soland's conjugate piror, stick-breaking prior is considered and the corresponding Bayesian estimators under the squared error loss function (quadratic loss) and LINEX loss function are obtained and compared with other estimators. The results may be of interest especially when only record values are stored.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.143-148
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• 2001

A dataset having missing observations is often completed by using imputed values. In this paper the performances and accuracy of complete case methods and four imputation procedures are evaluated when missing values exist only on the response variables in the Weibull regression model. Our simulation results show that compared to other imputation procedures, in particular, hotdeck and Weibull regression imputation procedure can be well used to compensate for missing data. In addition an illustrative real data is given.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.39-44
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• 2002

We compare MISE of the MLE, UMVUE, invariantly optimal estimator and Jacknife estimator for the reliability function of the Weibull distribution when the sample size is small.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.303-312
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• 2000

We compare the MLE, UMVUE and Jackknife Estimators for the reliability function of the weibull distribution when the sample size is small.