• Journal of the Korean Data and Information Science Society
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• 제24권6호
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• pp.1309-1317
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• 2013

일반화 지수분포 (generalized exponential distribution)를 따르는 점진 제 1종 구간 중도절단 (progressive type-I interval censoring) 표본에서 모수 추정은 Chen과 Lio (2010)가 최대우도 추정법 (maximum likelihood estimation), 중간점 근사법 (mid-point approximation method), EM 알고리즘 (expectation maximization algorithm), 적률 추정법 (method of moments estimation; MME)으로 하였으며, 그 방법들 중 평균제곱오차 (mean square error; MSE)가 가장 작은 추정법은 중간점 근사법이다. 하지만 중간점 근사법을 바탕으로 최대우도 추정법을 이용하여 모수를 추정하려고 한다면 모수에 대한 해를 전개할 수 없기 때문에 수치 해석적인 방법을 이용하여 추정하여야 한다. 본 논문에서는 이러한 문제를 해결하기 위해서 근사 최대우도 추정법 (approximate maximum likelihood estimation)을 이용하여 두 종류의 모수를 추정하고, 모의실험을 통하여 수치해석학적인 방법을 이용한 중간점 근사법의 해 (estimate of mid-point approximation method; MP)와 제시한 두 가지 추정량을 평균제곱오차 측면에서 비교한다.

• 한국지능시스템학회논문지
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• 제24권4호
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• pp.398-402
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• 2014

일반화된 삼각함수 퍼지집합은 삼각함수 퍼지수의 일반화이다. Zadeh([7])는 확률을 이용하여 퍼지이벤트에 대한 확률을 정의하였다. 우리는 정규분포와 지수분포를 각각 이용하여 실수 $\mathbb{R}$ 위에서 정규퍼지확률과 지수퍼지확률을 정의하고, 일반화된 삼각함수 퍼지집합에 대하여 정규퍼지확률과 지수퍼지확률을 계산하였다.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.1055-1065
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• 2003

We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with exponential lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.

• International Journal of Reliability and Applications
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• 제13권2호
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• pp.91-104
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• 2012

Sarhan and Kundu (2009) introduced a new distribution named as the generalized linear failure rate distribution. This distribution generalizes several well known distributions. The probability density function of the generalized linear failure rate distribution can be right skewed or unimodal and its hazard function can be increasing, decreasing or bathtub shaped. This distribution can be used quite effectively to analyze lifetime data in place of linear failure rate, generalized exponential and generalized Rayleigh distributions. In this paper, we apply the simulated annealing algorithm to obtain the maximum likelihood point estimates of the parameters of the generalized linear failure rate distribution. Simulated annealing algorithm can not only find the global optimum; it is also less likely to fail because it is a very robust algorithm. The estimators obtained using simulated annealing algorithm have been compared with the corresponding traditional maximum likelihood estimators for their risks.

• International Journal of Reliability and Applications
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• 제6권1호
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• pp.13-29
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• 2005

This paper deals with the stochastic analysis of a three-states semi-Markov reliability model. Using both the maximum likelihood and Bayes procedures, the parameters included in this model are estimated. Next, assuming that the lifetime and repair time are generalized exponential random variables, the reliability function of this system is obtained. Then, the distribution of the first passage time of this system is discussed. Finally, some of the obtained results are compared with those available in the literature.

• Communications for Statistical Applications and Methods
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• 제18권4호
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• pp.467-475
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• 2011

This paper develops the noninformative priors for the stress-strength reliability from one parameter generalized exponential distributions. When this reliability is a parameter of interest, we develop the first, second order matching priors, reference priors in its order of importance in parameters and Jeffreys' prior. We reveal that these probability matching priors are not the alternative coverage probability matching prior or a highest posterior density matching prior, a cumulative distribution function matching prior. In addition, we reveal that the one-at-a-time reference prior and Jeffreys' prior are actually a second order matching prior. We show that the proposed reference prior matches the target coverage probabilities in a frequentist sense through a simulation study and a provided example.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.1021-1028
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• 2006

We shall propose ML, ordinary jackknife and biased reducing estimators of the parameter in the right truncated exponential distribution with an unidentified uniform outlier when the truncated point is unknown and their biases and MSE's are compared numerically each other in the small sample sizes.

• Industrial Engineering and Management Systems
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• 제14권2호
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• pp.183-195
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• 2015

In this paper, we propose a two-stage group acceptance sampling plan for generalized inverted exponential distribution under truncated life test. Median life is considered as a quality parameter. Design parameters are obtained to ensure that true median life is longer than a given specified life at certain level of consumer's risk and producer's risk. We also explore situations under which design parameters based on median lifetime can be used for other percentile points. Tables and specific examples are reported to explain the proposed plans. Finally a real data set is analyzed to implement the plans in practical situations and some suggestions are given.

• Journal of the Korean Data and Information Science Society
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• 제25권2호
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• pp.393-402
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• 2014

수명시간 분석에서 자주 이용되는 분포 중 하나는 지수분포이다. 본 논문에서는 임의중도절단 자료의 분석에서 중도절단모형이 지수분포의 모수추정에 어떤 영향을 주는지에 대해서 알아보았다. 고려한 중도절단모형은 Koziol-Green 모형과 일반화 지수분포 모형으로 이들은 의미상 매우 다른 모형이다. 모의실험을 통해서 살펴본 결과 중도절단모형이 모수의 평균적인 추정값에는 크게 영향을 주지 않는다고 보이나 가정한 모형이 실제의 모형과 차이가 심하게 나는 경우 추정량의 MSE가 커지는 경향을 보였다.

• 충청수학회지
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• 제21권4호
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• pp.501-510
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• 2008

The distribution of the sum of n independent random variables having exponential distributions with different parameters ${\beta}_i$ ($i=1,2,{\ldots},n$) is given in [2], [3], [4] and [6]. In [1], by using Laplace transform, Jasiulewicz and Kordecki generalized the results obtained by Sen and Balakrishnan in [6] and established a formula for the distribution of this sum without conditions on the parameters ${\beta}_i$. The aim of this note is to present a method to find the distribution of the sum of n independent exponentially distributed random variables with different parameters. Our method can also be used to handle the case when all ${\beta}_i$ are the same.