• 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.15 no.2
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• pp.355-366
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• 2002

Nonparametric statistical inference for the parameter of logit model were examined. Usually nonparametric approach is milder than parametric approach based on normal theory assumption. We compared the two nonparametric methods for legit model, the bootstrap and random permutation in the sense of coverage probability. Monte Carlo simulation is conducted for small sample cases. Empirical power of hypothesis test and coverage probability for confidence interval estimation were presented for simple and multiple legit model respectively. An example were also introduced.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.867-889
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• 2014

Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1179-1185
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• 2003

시뮬레이션 상의 입력모델에 대한 기존의 연구는 과거의 자료를 바탕으로 선형의 모수적인 (parametric) 모형을 개발하는데 초점을 두고 있다. 그러나 이 경우에는 입력이 매우 복잡한 형태를 가지면 모수적인 모형을 잦는 것이 불가능해지므로 비모수적인(non-parametric) 접근방법이 절실한 실정이다 예로 인터넷 트래픽 모델의 시뮬레이션 수행시 입력으로 제공되는 단위 시간당 요구되는 웹 페이지의 수 같은 경우 데이터들 간데 종속관계가 매우 심하고 복잡하여 모수적 모형을 세우는데 어려움이 있다. 이러한 시스템들을 시뮬레이션 방법으로 분석 하고자 할 때, 기존의 trace-driven 시뮬레이션 방법이나 모수적 모형을 찾아 다수의 사실적인 시뮬레이션 입력 자료를 확보하는 것은 현실적으로 어려움이 있다. 따라서. 비모수적인 방법으로 다수의 사실적인 시뮬레이션 입력 자료를 생성하는 것이 필요하다. 이러한 비모수적인 방법에 대한 평가기준 설정은 시뮬레이션 상의 입력 모델에 대한 타당성을 제시한다는 점에서 또한 매우 중요하다. 본 논문에서는 붓트스트 랩의 방법중의 하나인 임계값 붓트스트랩을 이용하여 시뮬레이션 입력 자료 생성 방법을 개발하였고 Turing test를 통해 붓스트랩으로 생성산 입력 시나리오를 검증하였다.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.73-94
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• 2003

In general, the interest rate is forecasted by the parametric method which assumes the interest rate follows a certain distribution. However the method has a shortcoming that forecasting ability would decline when the interest rate does not follow the assumed distribution for the stochastic behavior of interest rate. Therefore, the nonparametric method which assumes no particular distribution is regarded as a superior one. This paper compares the interest rate forecasting ability between the two method for the Monetary Stabilization Bond (MSB) market in Korea. The daily and weekly data of the MSB are used during the period of August 9th 1999 to February 7th 2003. In the parametric method, the drift term of the interest rate process shows the linearity while the diffusion term presents non-linear decline. Meanwhile in the nonparametric method, both drift and diffusion terms show the radical change with nonlinearity. The parametric and nonparametric methods present a significant difference in the market price of interest rate risk. This means in forecasting the interest rate and the market price of interest rate risk, the nonparametric method is more appropriate than the parametric method.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.121-131
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• 1998

이 논문의 목적은 비모수 회귀모형에 있어서의 오차의 분산을 추정하는 방법들 중 차분에 기저한 방법 (difference-based methods)을 이용한 기존의 추정량들을 비교 분석하는데 있다. 특히 점근적인 최적 이차 차분에 기저한 Hall과 Kay, Titterington(1990)의 HKT 추정량에 대한 그들의 추정량에 대한 문제점들을 제시하고, HKT추정량과, GSJS추정량, Rice추정량에 대하여 모의 실험을 이용하여 모수에 대한 수렴 속도를 비교 분석 하였다. 또한 GSJS 추정량에 대한 일치성과 수렴 속도를 보였다.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.199-201
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• 2003

본 연구에서는 크리스프자료(crisp data)인 독립변수와 퍼지자료(fuzzy data)인 종속변수 사이의 관계가 특정한 함수로 표현되지 않는 비모수 퍼지회귀모형을 분석하기위하여 퍼지수 순위와 퍼지순위변환방법을 소개하고, 모의실험을 통하여 퍼지순위변환방법의 효율성을 조사한다.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.371-381
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• 2000

In this paper, we describes smoothing spline in nonparametric regression and some asymptotic results for estimates of the regression cofficients in the parametric part were biased on semi parametric regression estimator.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1269-1278
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• 2014

Despite the D-optimality criterion becomes very popular in designing an experiment for nonlinear models because of theoretical foundations it provides, it is very critical that the criterion depends on the unknown parameters of the nonlinear model. But some nonlinear models turned out to be partially nonlinear in sense that the optimal design depends on the subset of parameters only. It was a strong belief that the maximin approach to find a robust design to protect against the uncertainty of parameters is not guaranteed to be successful in nonlinear models. But the maximin approach could be a success for the partial nonlinear model, because often the optimal design depends on only one unknown value of parameter, easier to handle than the full parameters. We deal with maximin approach for Michaelis-Menten model with respect to D- and $D_s$-optimality.