• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.739-748
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• 2012

최소제곱 서포트벡터기계 (least squares support vector machine)는 분류 및 비선형 회귀분석에서 유용하게 사용되고 있는 통계적 기법이다. 본 논문에서는 각 집단별로 생존자료가 관측된 경우 적용할 수 있는 LS-SVM을 제안한다. 제안된 모형은 임의우측 중도절단자료를 비선형 회귀모형에 적용할 수 있게 Kaplan- Meier의 중도절단분포의 추정값을 이용하여 구해진 가중값을 사용하고, 집단 간의 변동을 나타내기 위하여 임의효과항을 포함한다. 벌칙상수와 커널모수의 최적값을 구하기 위하여 일반화 교차타당성함수가 사용되고 모의실험에서는 임의효과항을 포함하지 않은 LS-SVM과 성능을 비교함으로써 제안된 방법의 우수성을 보이기로 한다.

• Journal of the Korean Data and Information Science Society
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• 제4권
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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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• 제7권2호
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• pp.179-188
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• 1996

Accelerated life testing(ALT) of products quickly yields information on life. In this paper, we investigate asymptotic normalities of maximum likelihood(ML) estimators of parameters for ALT model under Type II censored data using results of Bhattacharyya(1985). Further illustrations include the treatment of asymptotic of the exponential and Weibull regression models.

• 품질경영학회지
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• 제18권1호
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• pp.9-20
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• 1990

The purpose of this paper is to propose the modified semiparametric estimators for survival function in the Cox's regression model with randomly censored data based on Tsiatis and Breslow estimators, and present their asymptotic variances estimates. The proposed estimators are compared to Tsiatis, Breslow, and Kaplan-Meier estimators through a small-sample Monte Carlo study. The simulation results show that the proposed estimators are preferred for small sample sizes.

• 디지털융복합연구
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• 제19권10호
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• pp.339-348
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• 2021

본 연구의 목적은 2010년부터 2019년까지 우리나라 10개의 서비스산업을 중심으로 자료포락분석 (DEA)을 이용하여 생산효율성을 측정하고 단절토빗회귀모형과 중도절단토빗회귀모형을 이용하여 그 결정요인을 분석하는 것이다. 분석결과, 첫째, 추정된 서비스산업의 효율성은 대체적으로 매우 낮았고 양극화되어 있었으며, '하수·폐기물 처리, 원료재생업'의 경우 기술적 비효율성이 규모의 비효율성에 기인하는 것을 알 수 있었다. 둘째, 결정요인 분석에서는 투자와 연구개발비는 기술효율성에 정(+)의 관계에 있고, 해외직접투자와 접대비는 부(-)의 영향을 주는 것으로 측정되었다. 또한, 규모가 큰 산업일수록 효율성도 높은 것으로 나타났다. 따라서, 우리나라 미래의 산업인 서비스산업의 발전을 위한 정부의 경제정책은 투자와 연구개발활동을 통한 거시정책과 해외직접투자와 인적자본의 융합 등 미시적인 부문이 동시에 고려되는 보다 융합적 정책이 필요하다.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.155-178
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• 2024

Regression discontinuity (RD) design is one of the most widely used methods in causal inference for estimation of treatment effect when the treatment is created by a cutpoint from the covariate of interest. There has been little attention to RD design, although it provides a very useful tool for analysis of treatment effect for censored data. In this paper, we define the causal effect for survival function in RD design when the treatment is assigned deterministically by the covariate of interest. We propose estimators of this causal effect for survival data by using transformation, which leads unbiased estimator of the survival function with local linear regression. Simulation studies show the validity of our approach. We also illustrate our proposed method using the prostate, lung, colorectal and ovarian (PLCO) dataset.

• 한국산업보건학회지
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• 제32권1호
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• pp.21-30
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• 2022

Objectives: The purpose of this study is to suggest an optimal method by comparing the analysis methods of work environment measurement datasets including left-censored data where one or more measurements are below the limit of detection (LOD). Methods: A computer program was used to generate left-censored datasets for various combinations of censoring rate (1% to 90%) and sample size (30 to 300). For the analysis of the censored data, the simple substitution method (LOD/2), β-substitution method, maximum likelihood estimation (MLE) method, Bayesian method, and regression on order statistics (ROS)were all compared. Each method was used to estimate four parameters of the log-normal distribution: (1) geometric mean (GM), (2) geometric standard deviation (GSD), (3) 95th percentile (X95), and (4) arithmetic mean (AM) for the censored dataset. The performance of each method was evaluated using relative bias and relative root mean squared error (rMSE). Results: In the case of the largest sample size (n=300), when the censoring rate was less than 40%, the relative bias and rMSE were small for all five methods. When the censoring rate was large (70%, 90%), the simple substitution method was inappropriate because the relative bias was the largest, regardless of the sample size. When the sample size was small and the censoring rate was large, the Bayesian method, the β-substitution method, and the MLE method showed the smallest relative bias. Conclusions: The accuracy and precision of all methods tended to increase as the sample size was larger and the censoring rate was smaller. The simple substitution method was inappropriate when the censoring rate was high, and the β-substitution method, MLE method, and Bayesian method can be widely applied.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.227-233
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• 2004

This paper presents our work on constructing a model that is intended to predict the probability of attrition at specified points in time among customers of an insurance company. There are some difficulties in building a data-based model because a data set may contain possibly censored observations. In an effort to avoid such kind of problem, we performed logistic regression over specified time intervals while using explanatory variables to construct the proposed model. Then, we developed a Cox-type regression model for estimating the probability of attrition over a specified period of time using time-dependent explanatory variables subject to changes in value over the course of the observations.

• Journal of the Korean Statistical Society
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• 제26권3호
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• pp.299-308
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• 1997

In the simple linear regression model Y = .alpha.$_{0}$ + .beta.$_{0}$Z + .epsilon. under the right censorship of the response variables, the estimation of the mean lifetime E(Y) is an interesting problem. In this paper we propose a method of estimating E(Y) based on the observations modified by the arguments of Buckley and James (1979). It is shown that the proposed estimator is consistent and our proposed procedure in the simple linear regression case can be naturally extended to the multiple linear regression. Finally, we perform simulation studies to compare the proposed estimator with the estimator introduced by Gill (1983).83).

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1151-1160
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• 2014

In DNA microarray studies, the number of genes far exceeds the number of samples and the gene expression measures are highly correlated. Partial least squares regression (PLSR) is one of the popular methods for dimensional reduction and known to be useful for the classifications of microarray data by several studies. In this study, we suggest a modified version of the partial least squares regression to analyze gene expression data with survival information. The method is designed as a new gene selection method using PLSR with an iterative procedure of imputing censored survival time. Mean square error of prediction criterion is used to determine the dimension of the model. To visualize the data, plot for variables superimposed with samples are used. The method is applied to two microarray data sets, both containing survival time. The results show that the proposed method works well for interpreting gene expression microarray data.