• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.569-580
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• 2015

두 집단의 생존분포의 비교를 위한 방법으로는 로그-순위 검정법이 널리 쓰이고 있다. 그러나 이는 두 집단의 생존분포 간에 비례위험모형 가정이 성립하는 경우에 적합한 것으로써, 두 집단의 생존분포가 교차하는 상황 하에서는 해당 검정법의 유효성을 장담할 수 없다. 그러나 두 집단의 생존분포가 교차하는 상황은 빈번히 발생하며, 이 같은 상황에서 두 생존분포의 동일성 검정을 위한 여러 연구가 진행되어왔다. 본 논문에서는 두 집단 간 생존분포가 교차하는 상황 하에서의 동일성 검정을 위한 방법들을 고려한다. 나아가 교차하는 상황을 위치에 따라 세분화하고 모의실험을 통해 각 상황에서 다양한 검정법들의 검정력을 비교하였으며, 그 결과를 토대로 주어진 상황에서 적절한 방법의 선택에 유용한 정보를 제공하고자 한다.

• 응용통계연구
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• 제22권6호
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• pp.1345-1353
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• 2009

We introduce a simple methodology, so-called generalized gamma-polynomial approximation, based on moment-matching technique to approximate survival and hazard functions in the context of parametric survival analysis. We use the generalized gamma-polynomial approximation to approximate the density and distribution functions of convolutions and finite mixtures of random variables, from which the approximated survival and hazard functions are obtained. This technique provides very accurate approximation to the target functions, in addition to their being computationally efficient and easy to implement. In addition, the generalized gamma-polynomial approximations are very stable in middle range of the target distributions, whereas saddlepoint approximations are often unstable in a neighborhood of the mean.

• Communications for Statistical Applications and Methods
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• 제24권2호
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• pp.115-127
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• 2017

This paper proposes a Bayesian test for the equivalence of survival functions in multiple groups. Proposed Bayesian test use the model of Cox's regression with time-varying coefficients. B-spline expansions are used for the time-varying coefficients, and the proposed test use only the partial likelihood, which provides easier computations. Various simulations of the proposed test and typical tests such as log-rank and Fleming and Harrington tests were conducted. This result shows that the proposed test is consistent as data size increase. Specifically, the power of the proposed test is high despite the existence of crossing hazards. The proposed test is based on a Bayesian approach, which is more flexible when used in multiple tests. The proposed test can therefore perform various tests simultaneously. Real data analysis of Larynx Cancer Data was conducted to assess applicability.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.31-38
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• 2002

In the case of multiple survival times which might be censored at each covariate vector, we study the regression quantile estimations in this paper. The estimations are based on the empirical distribution functions of the censored times and the sample quantiles of the observed survival times at each covariate vector and the weighted least square method is applied for the estimation of the regression quantile. The estimators are shown to be asymptotically normally distributed under some regularity conditions.

• 한국산림과학회지
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• 제88권3호
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• pp.320-326
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• 1999

연구에서는 잔존목의 본수에 대한 수식형태 및 고사율 추정 미분함수형태를 가정하여 인공림에 대한 잔존목을 추정하기 위한 15개의 함수들을 적분 및 지수함수형태로 유도하였다. 또한 이 모델들을 간벌이 되지 않은 스트로부스 잣나무 인공림의 반복측정 자료를 이용하여 모델의 적용성을 검토하였다. 그 결과 $t^{\beta}$와 같이 임령의 지수형태를 포함하는 3개의 함수들이 시간에 따른 자연 간벌효과를 설명하는데 상대적으로 유효한 것으로 나타났다. 한편 지위가 자연 간벌에 미치는 효과를 분석하기 위하여 낙엽송 임분의 잔존목 추정을 위한 함수를 유도하였다. 그 결과 $N_{min}$이 지위지수와 부의 상관관계를 가지는 것으로 나타났고, 초기 임분밀도를 같은 값으로 가정한 경우에도 잔존목 추정함수의 곡선이 지위지수별로 달라짐을 알 수 있었다.

• Genomics & Informatics
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• 제17권4호
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.179-185
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• 1994

To test the equality of survival distributions in the presence of arbitrary right censorship, the choice of weights which are functions of the number of individuals at risk at the time of each death is very important in increasing the power of the test. In this paper a weight by probit scale is derived and the efficiencies relative to the other weight's are also investigated.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권1호
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• pp.56-63
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• 2016

Purpose: The purpose of this study is to point out that the Kaplan-Meier method is not valid to calculate the survival probability or failure probability (risk) in the presence of competing risks and to introduce more valid method of cumulative incidence function. Methods: Survival analysis methods have been widely used in biostatistics division. However the same methods have not been utilized in reliability division. Especially competing risks cases, where several causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not noticed in the realm of reliability expertism or they are analysed in the wrong way. Specifically Kaplan-Meier method which assumes that the censoring times and failure times are independent is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced and sample competing risks data are analysed using cumulative incidence function and some graphs. Finally comparison of cumulative incidence functions and regression type analysis are mentioned briefly. Results: Cumulative incidence function is used to calculate the survival probability or failure probability (risk) in the presence of competing risks and some useful graphs depicting the failure trend over the lifetime are introduced. Conclusion: This paper shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime. In stead, cumulative incidence function is shown to be useful. Some graphs using the cumulative incidence functions are also shown to be informative.

• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.425-445
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• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

• Communications for Statistical Applications and Methods
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• 제20권3호
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• pp.169-174
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• 2013

The ROC curve is drawn with two conditional cumulative distribution functions (or survival functions) of the univariate random variable. In this work, we consider joint cumulative distribution functions of k random variables, and suggest a ROC curve for multivariate random variables. With regard to the values on the line, which passes through two mean vectors of dichotomous states, a joint cumulative distribution function can be regarded as a function of the univariate variable. After this function is modified to satisfy the properties of the cumulative distribution function, a ROC curve might be derived; moreover, some illustrative examples are demonstrated.