• 대한산업공학회지
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• 제24권3호
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• pp.367-379
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• 1998

This paper considers two modes of partially accelerated life tests for items having Weibull lifetime distributions. In a use-to-acclerated mode each item is first run at use condition and, if it does not fail for a specified time, then it is run at accelerated condition until a predetermined censoring time. In an accelerated-to-use mode each one is first run at accelerated condition and, if it does not fail for a specified time, then it is run at use condition. Maximum likelihood estimators of the parameters of the lifetime distribution at use condition, and the 'acceleration factor' are obtained. The stress change time for each mode is determined to minimize the asymptotic variance of the acceleration factor, and the two modes are compared. For selected values of the design parameters the optimum plans are obtained, and the effects of the incorrect pre-estimates of the design parameters are investigated. Minimizing the generalized asymptotic variance of the estimators of the model parameters is also considered as an optimality criterion.

• 품질경영학회지
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• 제26권1호
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• pp.74-87
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• 1998

This study considers optimal design of accelerated life tests under constant stress using that the first passage time to cross a critical boundary through amount of accumulated degradation has an inverse Gaussian distribution when the degradation process follows to a Brownian motion with positive drift of log linear function of stress. Optimum plans for Type I censoring are derived by minimizing the asymptotic variance of estimated quantiles at the use stress. Sensitivity analyses are also conducted to see how sensitive the optimality criterion is with respect to the uncertainties involved in the guessed values.

• 응용통계연구
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• 제25권4호
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• pp.621-632
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• 2012

Recurrent event data can be easily found in longitudinal studies such as clinical trials, reliability fields, and the social sciences; however, there are a few observations that disappear temporarily in sight during the follow-up and then suddenly reappear without notice like the Young Traffic Offenders Program(YTOP) data collected by Farmer et al. (2000). In this article we focused on inference for a cumulative mean function of the recurrent event data with these incomplete observation gaps. Defining a corresponding risk set would be easily accomplished if we know the exact intervals where the observation gaps occur. However, when they are incomplete (if their starting times are known but their terminating times are unknown) we need to estimate a distribution function for the terminating times of the observation gaps. To accomplish this, we treated them as interval-censored and then estimated their distribution using the EM algorithm proposed by Turnbull (1976). We proposed a nonparametric estimator for the cumulative mean function and also a nonparametric test to compare the cumulative mean functions of two groups. Through simulation we investigated the finite-sample performance of the proposed estimator and proposed test. Finally, we applied the proposed methods to YTOP data.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.431-443
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• 2019

Goodness-of-fit techniques are an important topic in statistical analysis. Censored data occur frequently in survival experiments; therefore, many studies are conducted when data are censored. In this paper we mainly consider test statistics based on the empirical distribution function (EDF) to test normal distributions with unknown location and scale parameters when data are randomly censored. The most famous EDF test statistic is the Kolmogorov-Smirnov; in addition, the quadratic statistics such as the $Cram{\acute{e}}r-von$ Mises and the Anderson-Darling statistic are well known. The $Cram{\acute{e}}r-von$ Mises statistic is generalized to randomly censored cases by Koziol and Green (Biometrika, 63, 465-474, 1976). In this paper, we generalize the Anderson-Darling statistic to randomly censored data using the Kaplan-Meier estimator as it was done by Koziol and Green. A simulation study is conducted under a particular censorship model proposed by Koziol and Green. Through a simulation study, the generalized Anderson-Darling statistic shows the best power against almost all alternatives considered among the three EDF statistics we take into account.

• 응용통계연구
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• 제33권6호
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• pp.777-789
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• 2020

COVID-19는 지난 2019년 12월부터 중국에서 발생하여 전세계적으로 확산된 대유행병이 되었다. 본 연구에서는 한국 질병 관리 본부에서 공개한 오픈 자료를 이용하였으며 시각화 기법을 통해 확진자의 남녀별 지역별 추세를 조사하였다. 또한 평균 바이러스 잠복기간을 추정하기 위해 감염원이 알려진 두 감염 그룹의 증상 발현 시점과 양성 확진 시점을 활용하였다. 하지만 양성 확진자 중 86%가 무증상으로 정확한 증상 발현시점을 알 수 없었다. 또한 주어진 자료에서는 감염시점도 알려져 있지 않아 감염시점과 증상 발현 시점차로 정의되는 잠복기간은 정확하게 측정하기가 어렵다. 이에 생존 분석의 한 기법인 구간 중도 절단을 적용하여 잠복기간의 분포를 추정하였다. 여러가지 모수 분포를 적용한 결과 최적의 분포하에서 평균 잠복 기간은 5.4일 (95% 신뢰구간 (4.70,6.01)일)이었다. 본 분석에서는 확진자 표본을 이용하여 치사율과 치유율을 구하기 위해 경쟁 위험 모형을 적용하였다. 분석 결과 50대이상의 치사 위험률은 50대미만 그룹의 30배이상이며 남성 양성 확진자가 사망할 확률이 더 높았다. 또한 여성이고 나이가 젊고 무증상일 때 치유될 가능성이 더 높았다.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제5권2호
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• pp.123-132
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• 1998

Let F and G denote the distribution functions of the failure times and the censoring variables in a random censorship model. Susarla and Van Ryzin(1978) verified consistency of $F_{\alpha}$, he NPBE of F with respect to the Dirichlet process prior D($\alpha$), in which they assumed F and G are continuous. Assuming that A, the cumulative hazard function, is distributed according to a beta process with parameters c, $\alpha$, Hjort(1990) obtained the Bayes estimator $A_{c,\alpha}$ of A under a squared error loss function. By the theory of product-integral developed by Gill and Johansen(1990), the Bayes estimator $F_{c,\alpha}$ is recovered from $A_{c,\alpha}$. Continuity assumption on F and G is removed in our proof of the consistency of $A_{c,\alpha}$ and $F_{c,\alpha}$. Our result extends Susarla and Van Ryzin(1978) since a particular transform of a beta process is a Dirichlet process and the class of beta processes forms a much larger class than the class of Dirichlet processes.