• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.643-653
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• 2013

The most important procedure in frequency analysis is to determine the appropriate probability distribution and to estimate quantiles for a given return period. To perform the frequency analysis, the goodness-of-fit tests should be carried out for judging fitness between obtained data from empirical probability distribution and assumed probability distribution. The previous goodness-of-fit could not consider enough extreme events from the recent climate change. In this study, the critical values of the modified Anderson-Darling test statistics were derived for 3-parameter Weibull distribution and power test was performed to evaluate the performance of the suggested test. Finally, this method was applied to 50 sites in South Korea. The result shows that the power of modified Anderson-Darling test has better than other existing goodness-of-fit tests. Thus, modified Anderson-Darling test will be able to act as a reference of goodness-of-fit test for 3-parameter Weibull model.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.13-30
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• 1993

We propose the ordered least squares estimators(OLSE's) of the parameters and the p-th quantiles for the two-parameter Weibull regression model under the Type II censoring, The Monte Carlo simulations are performed to compare the proposed estimators with the maximum likelihood estimators(MLE's), and it is shown that the proposed estimators are slightly better than MLE's as the censoring rate goes up.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.261-270
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• 1997

This paper is proposed the parametric empirical Bayes(EB) confidence intervals which corrects the deficiencies in the naive EB confidence intervals of the scale parameter in the Weibull distribution under item-censoring scheme. In this case, the bootstrap EB confidence intervals are obtained by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive EB confidence intervals through Monte Carlo study are also presented.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.169-180
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• 1999

A Markov Chain Monte Carlo method with development of computation is used to be the software system reliability probability model. For Bayesian estimator considering computational problem and theoretical justification we studies relation Markov Chain with Gibbs sampling. Special case of GOS with Superposition for Goel-Okumoto and Weibull models using Gibbs sampling and Metropolis algorithm considered. In this paper discuss Bayesian computation and model selection using posterior predictive likelihood criterion. We consider in this paper data using method by Cox-Lewis. A numerical example with a simulated data set is given.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09b
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• pp.1146-1147
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• 2006

Diamond segments were fabricated by cold pressing and sintering under pressure at the temperature up to $750^{\circ}C$. Based on the results of this investigation, it can be concluded that the segments containing 39wt.% cobalt in the matrix material have the highest bending strength at a fracture probability of 50 % due to the weibull distribution method. According to the weibull statistics, it was also determined that the transverse rupture strength was the best for 39 wt.% cobalt ratio in the matrix material for the fracture probability when the other variables are the same.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.193-198
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• 1999

The estimation of parameters in regression models with multiplicative errors is usually based on the gamma or log-normal likelihoods. Under reciprocal misspecification, we compare the small sample efficiencies of two sets of estimators via a Monte Carlo study. We further consider the case where the errors are a random sample from a Weibull distribution. We compute the asymptotic relative efficiency of quasi-likelihood estimators on the original scale to least squares estimators on the log-transformed scale and perform a Monte Carlo study to compare the small sample performances of quasi-likelihood and least squares estimators.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.49-57
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• 2004

In this paper, we calculate the premium rate of reliability insurance policy for wiper motors under the assumption of Weibull physics of failure. We also describe the performance factors which have an effect on failure characteristics of wiper motors. The maximum likelihood estimates of shape parameter and scale parameter are obtained by using interval censored real data of sample sizes 6 using MINITAB.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.140-146
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• 2007

In this paper, we consider a periodic imperfect preventive maintenance(PM) policy in which the system's failure rate after each PM remains unchanged. The system undergoes only minimal repairs at failures between PMs. Exact mathematical formula of the expected cost rate per unit time is derived. Optimal number of PMs and optimal maintenance period are derived by minimizing the expected cost rate per unit time. A numerical example is provided to illustrate the proposed approach under Weibull lifetime distribution.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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• pp.16-23
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• 2006

Recently the design of multi-stage gear drive is being highly concerned. Until now, since the researches of gear drive are focused on the design for satisfying safety factor, the reliability evaluation of multi-stage gear drive is not included. In this paper, the life and reliability models of multi-stage gear drive are proposed using methods of probability and statistics. The life and reliability of the multi-stage gear drive have been evaluated, which is based on the life and reliability of each stage gear drive. The pinion and gear lives of each stage are calculated using the Lundberg-Palmgren theory and the Weibull failure distribution. These lives are combined using methods of probability and statistics to produce a life and reliability model of multi-stage gear drive.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.147-161
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• 2016

This paper introduces the four parameter new generalized inverse Weibull distribution and investigates the potential usefulness of this model with application to reliability data from engineering studies. The new extended model has upside-down hazard rate function and provides an alternative to existing lifetime distributions. Various structural properties of the new distribution are derived that include explicit expressions for the moments, moment generating function, quantile function and the moments of order statistics. The estimation of model parameters are performed by the method of maximum likelihood and evaluate the performance of maximum likelihood estimation using simulation.