• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.157-169
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• 2016

We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.315-324
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• 2003

Stream flow data was analyzed for determining the lowflow which is the standard for river maintenance flow. Lowflow quantiles were estimated based on the parametric and nonparametric methods and two methods were compared by Monte Carlo simulation study. As the results of the parametric method, three probability distributions such as gamma-2, lognormal-2 and Weibull-2, are selected as appropriate models for stream flow data of 13 stations in Han River Basins. According to simulation results, relative bias (RBIAS) and relative root mean square error (RRMSE) of the lowflow quantiles are the smallest when the applied and population models are the same. The fame statistical properties from the nonparametric models are good within the interpolation range. Among 7 bandwidth selectors used in this study, the RRMSEs of the Park and Marron method (PM) are the smallest while those of the Shoaler and Jones method (SJ) are the largest.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.325-330
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• 2018

This paper, following the shape parameters of the minimax distribution, describes the special form of the beta distribution, the Minimax distribution, as a function of the shape parameters for the software reliability model based on the non-homogeneous Poisson process. Characteristics and usefulness were discussed. As a result, the case of the shape parameter 1 of Minimax distribution than less than and greate in mean squared error is the smallest, in determination coefficient, appears to be high, the shape parameter 1 of Minimax distribution regard as an efficient model. The estimated determination coefficient of the proposed model is estimated to be more than 95%, which is a useful model in the field of software reliability. Through this study, software design and users can identify the software failure characteristics using mean square error, decision coefficient, and confidence interval can be used as a basic guideline.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.685-694
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• 1998

임상실험이나 신뢰성공학 분야에서 임의 중단자료를 이용한 비모수적 신뢰도 추정량으로 Kaplan-Meier 추정량과 Nelson형 추정량이 많이 사용되고 있다. 그러나 Nelson형 추정량은 평균제곱오차의 관점에서 Kaplan-Meier 추정량보다 추정능력이 우수한 반면 편의는 신뢰도가 감소함에 따라 양의 방향으로 점증하는 소표본 특성을 갖는다. Nelson형 추정량의 이러한 특성 때문에 신뢰도의 함수로 표현되는 잔여수명 분위수함수 등의 추정시에는 평균제곱오차의 관점에서 Kaplan-Meier 추정량보다 추정능력이 떨어짐을 볼 수 있다. 이러한 점을 고려하여 이 두 추정량을 가중평균으로 통합한 새로운 비모수적 신뢰도 추정량을 제안하고 추정량의 특성을 비교 분석하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.153-161
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• 1993

시간변동모수 공적분(Time-Varying Parameter Cointegration) 모형에서 시간변동모수가 안정적인 확률과정을 따르는 경우 BI(bi-integrated) 과정을 오차항으로 갖는 고정모수 공적분 모형과 동일하다. 이 때 BI과정은 ARCH 과정과 조건부분산이 동치관계에 있음을 이용하여 소득과 비내구재(서비스) 소비의 시간변동모수 공적분 관계를 추정하였다. 이로부터 합리적기대 항상소득가설을 검증한 결과 고정모수 공적분 모형과 달리 가설을 기각할 수 없는 것으로 나타났다.

• 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.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.255-264
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• 2008

It is common in practice to use mean squared error(MSE) for prediction. Recently, Park and Shin (2005) and Jones et al. (2007) studied prediction based on mean squared relative error(MSRE). We proposed a new nonparametric way of prediction based on MSRE substituting Jones et al. (2007) and provided a small simulation study which highly supports the proposed method.

• 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.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.185-190
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• 1999

비모수 회귀모형에 있어서의 오차분산을 추정하는 방법들 중 차분에 기저한 방법(difference-based methods)을 이용한 기존의 추정량들을 비교 분석하는데 목적이 있다. 특히 점근적인 최적 이차차분에 기저한 Hall과 Kay, Titterington(1990)의 HKT 추정량에 대한 그들의 추정량에 대한 문제점들을 제시하고, HKT추정량과, GSJS 추정량, Rice 추정량에 대하여 모의실험을 이용하여 모수에 대한 수렴속도를 비교 분석하였다. 또한 GSJS 추정량에 대한 일치성과 수렴 속도를 보였다.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.341-353
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• 1993

As an estimator of the conditional probability of discovering a new species at the next observation after a sample of certain size is taken, the one proposed by Good(1953) has been most widely used. Recently, Clayton and Frees(1987) showed via simulation that their nonparametric maximum likelihood estimator(NPMLE) has smaller MSE than Good's estimator when the population is relatively nonuniform. Lee(1989) proved that their conjecture is asymptotically true for truncated geometric population distributions. One shortcoming of the NPMLE, however, is that it has a considerable amount of negative bias. In this study we proposed a bias-corrected version of the NPMLE for virtually all realistic population distributions. We also showed that it has a smaller asymptotic MSE than Good's extimator except when the population is very uniform. A Monte Carlo simulation was performed for small sample sizes, and the result supports the asymptotic results.