• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.357-370
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• 2022

In this paper, Tobit and Heckit models are introduced. These models have been used for analyzing censored data. Censoring occurs at a specific point and a large number of observations are distributed with a positive probability at a certain point. Censoring can occur due to observing limitation or exogenous variables. Tobit and Heckit models are used to correct sample selection bias, which can occur when an ordinary linear regression model is fitted to censored data. However, the difference between the two models is not clearly accounted for; hence, they have often been used interchangeably. Therefore, the suitability of the models was validated through simulated data, and demonstrated through real data. As the result, it was confirmed that both Tobit and Heckit models are well-fitted to the data censored due to observing limitation, although Tobit model was fitted parsimoniously. In contrast, only Heckit model is well-fitted to the data censored due to exogenous variables.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.521-531
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• 2010

Interval-censored observations are common in medical and epidemiologic studies; however, limited studies exist due to the complexity and special structure of interval-censoring. This paper introduces the imputation method and the self consistency method in the interval-censored data. We propose a new method of generating random numbers under an interval-censoring set-up. Through simulation studies we compare two methods under various simulation schemes in the sense of the mean squared error for estimating the median survival time and the mean integrated squared error for estimating the survival function. Under a moderate censoring percentage, the mean imputation method showed a better performance than the self-consistency method in estimating the median survival time and the survival function.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.39-52
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• 2007

Crossover trials of new drugs in the treatment of angina pectoris, which frequently use treadmill exercise test for the assessment of its efficacy, produce censored survival times. In this paper we consider analysis approaches for censored survival times from crossover trials. Previously, a stratified Cox model for paired observation and nonparametric methods have been presented as possible analysis methods. On the other hand, the differences of two survival times would produce interval-censored survival times and we propose a Cox model for interval-censored data as n alternative analysis method. Example data is analyzed in order to compare these different methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.357-361
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• 2010

미래의 연별 최대 강수량 예측의 정확성을 향상시키는데 역사적 자료가 도움이 된다는 많은 연구 결과가 있었다. 관측의 오차와 자료의 손실로 역사자료를 이용한 강수 예측 방법은 절단자료의 분석을 중심으로 연구되었다. 대표적인 역사자료의 이용방법으로 조건부 적률을 이용한 B17B [Interagency Committee in Water Data, 1982], 조건부적률과적률 관계식을 이용한 Expected Moment Algorithm(EMA) [Cohn et al.;1997], 조건부 확률가중적률을 이용한 Partial Probability Weighted Moment (PPWM)[Wang ; 1991] 방법이 있다. 본 연구에서는 역사적 자료를 반영하는 방법에 있어 B17B와 EMA의 관계를 밝히고 그러한 관계가 PPWM에 동일하게 적용할 수 있음을 보였다. 우리는 B17B와 EMA의 관계를 적률방정식으로 표현하였고 PPWM에서 확률가중 적률 방정식을 정의함으로써 PPWM을 확장하였다. 본 연구에서 제안한 새로운 역사 자료를 이용한 강수예측 방법론을 Expected Probability Weighted Momemt (EPWM) 방법이라고 부르고 그 예측 방법의 성능을 다른 예측방법과 시뮬레이션 결과를 통해 비교하였다. 역사 자료 방법론의 비교는 Generalized Extreme Value (GEV) 분포를 이용하여 이루어졌으며, 각 방법론은 GEV분포의 형태모수(shape parameter)따라 다른 특성을 나타난다는 것을 보였다. 뿐만 아니라 여기서 제안한 EPWM 방법은 대부분의 경우에 좋은 추정량을 준다는 것을 보였다.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.761-769
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• 2018

Simulations are important for survival analyses that deal with censored data. Cox models are widely used in survival analyses, therefore, we investigate how to generate censored data that can simulate the Cox model. Bender et al. (Statistics in Medicine, 24, 1713-1723, 2005) provided a parametric method for generating survival times, but we need to generate censoring times as well as survival times to simulate the censored data. In addition to the parametric method for generating censored data, a nonparametric method is also proposed and applied to a real data set.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.753-761
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• 2019

A test procedure based on a Kendall's τ statistic is proposed for the association of bivariate interval censored data. In particular, a leverage bootstrap technique is applied to replace unknown failure times and a classical adjustment method is applied for treating tied observations. The suggested method shows desirable results in simulation studies. An AIDS dataset is analyzed with the suggested method.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.823-833
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• 2016

The lifetime is a key characteristic of product quality. It is best to obtain the lifetime data of all samples, but they are often censored due to time or expense limitations. In this paper, we propose a binomial cumulative sum (CUSUM) chart to monitor the mean of type I right-censored Weibull lifetime data, for a xed value of the Weibull shape parameter. We compare the performance of the proposed binomial CUSUM chart with CUSUM charts studied previously using the steady-state average run length (ARL). The results show that the performance of the binomial CUSUM chart is better when the censoring rate is high and/or the sample size is small.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.883-891
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• 2011

For survival data we sometimes want to test a log normality hypothesis that can be changed into normality by transforming the survival data. Hence the Shapiro-Wilk type statistic for normality is generalized to randomly censored data based on the Kaplan-Meier product limit estimate of the distribution function. Koziol and Green (1976) derived Cram$\acute{e}$r-von Mises statistic's randomly censored version under the simpl hypothesis. These two test statistics are compared through a simulation study. As for the distribution of censoring variables, we consider Koziol and Green (1976)'s model and other similar models. Through the simulation results, we can see that the power of the proposed statistic is higher than that of Koziol-Green statistic and that the proportion of the censored observations (rather than the distribution of censoring variables) has a strong influence on the power of the proposed statistic.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.251-255
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• 2005

중도절단된 자료와 표본수가 적은 자료를 가지는 생존분석에서 생존율을 추정하거나 두 집단의 생존율을 비교할 때 정규분포 근사를 가정한 신뢰구간을 이용하는 데는 많은 어려움이 생긴다. 생존함수의 신뢰구간에 대한 중도절단을, 표본의 크기에 따른 다양한 상황의 모의실험을 통하여 Kaplan-Meier, Nelson, 적률 추정량 그리고 cox model의 ${\beta}$을 가지고 붓스트랩을 이용한 신뢰구간과 비모수 신뢰구간, 우도비 신뢰구간의 실제 포함 확률을 비교해보고자 한다.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.127-132
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• 2002

절단된 정규분포의 평균과 분산을 추정하기 위하여 전체 표본에 기초한 최대가능도 추정량을 사용한 방법과 절단된 후에 남아있는 표본만을 고려한 절단된 표본의 표본평균과 표본분산을 시뮬레이션을 통해 비교 연구하였다. 평균을 추정하는 경우에는 놀랍게도 절단된 자료에 기초한 추정량이 전체 표본에 기초한 추정량보다 평균제곱오차가 더 작다는 것을 발견하였다.