• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.357-371
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• 2007

For the time-to-failure data with competing risks, cumulative incidence functions (CIFs) are commonly estimated using nonparametric methods. If the cases of events due to the cause of primary interest are infrequent relative to other cause of failure, nonparametric methods may result in rather imprecise estimates for CIF. In such cases, Bryant et al. (2004) suggested to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimator. We represented the semiparametric cumulative incidence estimator and extended to the model of Weibull and log-normal distribution. We also conducted simulations to access the performance of the semiparametric cumulative incidence estimators and to investigate the impact of model misspecification in log-normal cause-specific hazard model.

• Environmental and Resource Economics Review
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• v.14 no.2
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• pp.447-474
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• 2005

Respondents, in the willingness to pay (WTP) survey, may have preference intensity about their stated WTP values. This study elicited a post-decisional intensity measure for each observed WTP answer for gathering information on the degree of preference intensity. In order to deal with the WTP data with preference intensity, this paper considers using the Type 3 Tobit model. This is usually estimated by the parametric two-stage estimation method assuming homoskedastic and bivariate normal error structure. However, if the assumptions are not satisfied, the estimates are inconsistent. The author has tested the hypotheses of homoskedasticity and normality, and could not accept them at the 1% level. The assumptions required to estimate the parametric Type 3 model are, therefore, too strong to be satisfied. As an alternative the parametric model, this study applies a semiparametric Type 3 Tobit model. The results show that the semiparametric model significantly outperforms the parametric model, and that more importantly, the mean WTP from the parametric model is significantly different from that from the semiparametric model.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.71-79
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• 2013

Semiparametric and nonparametric small area estimations have been studied to overcome a large variance due to a small sample size allocated in a small area. In this study, we investigate semiparametric and nonparametric mixed effect small area estimators using penalized spline and kernel smoothing methods respectively and compare their performances using labor statistics.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.267-277
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• 2016

This paper considers we consider the estimation of copula parameters based on residuals in stochastic regression models. We prove that a semiparametric estimator using residual empirical distributions is consistent under some conditions and apply the results to the copula-ARMA model. We provide simulation results for illustration.

• Environmental and Resource Economics Review
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• v.12 no.4
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• pp.545-557
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• 2003

A new semiparametric estimator of a dichotomous choice contingent valuation model is proposed by adapting the well-known density weighted average derivative of the regression function. A small sample behavior of the estimator is demonstrated very briefly by a simulation and the estimator is applied to estimate the WTP for preserving the Dong River area in Korea.

• Environmental and Resource Economics Review
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• v.6 no.2
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• pp.233-258
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• 1997

본 논문은 가상가치기법(CVM)을 이용하여 대구시 수도물 수질개선의 경제적 편익에 대한 결정요인을 분석하고자 한다. 자료는 양분선택형 설문조사자료이며, 추정기법으로서 단일지수모형구조(single-index model)를 가정하는 두개의 준모수 추정법이 원용되었다. 비교목적으로 양분선택형 가상가치기법 문헌에서 전통적으로 사용되어 온 probit모형에 의한 추정결과도 아울러 제시된다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.605-617
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• 2014

Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2006.11b
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• pp.411-416
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• 2006

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.777-792
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• 1998

병원의 임상연구실험에서 종종 환자들의 치료에 따른 병세의 호전상태를 여러단계로 분류하여 상이한 치료방법에 대한 치료효과간의 차이론 알고자 하는 경우가 있다. 이와 같이 다중상태의 생존자료를 분석하기 위해서 본 논문에서는 semi-Markov 모형에 Cox 회귀모형을 적용하여 회귀계수와 기저생존함수를 추정하고 이를 바탕으로 반응확률함수를 추정하였다. 그리고 본 논문의 결과를 실제 임상실험에서 얻어진 자료에 적용하여 분석하였다.