• 대한수학회논문집
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• 제8권4호
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• pp.779-791
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• 1993

• 품질경영학회지
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• 제17권2호
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• pp.70-80
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• 1989

A nonparametric test for testing the parallelism of regression lines against ordered alternatives is proposed. The proposed test statistic is based on a linear combination of robust slope estimators. It is a modified version of the Adichie's test statistics based on scores. A snail-sample Monte Carlo study shows that the proposed test is compatible with the Adichie's test.

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.39-46
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• 1996

Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. In this paper we compare power of goodness-of-fit test statistics for testing the (null)parametric model versus the (alternative) nonparametric model.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 2002년도 제54차 추계 학술대회 연제집
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• pp.447-448
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• 2002

• 응용통계연구
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• 제25권2호
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• pp.321-330
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• 2012

In this paper we study some basic statistics in quantile regression. In particular, we investigate the residual, goodness-of-fit statistic and the effect of one or few observations on estimates of regression coefficients. In addition, we compare the proposed goodness-of-fit statistic with the statistic considered by Koenker and Machado (1999). An illustrative example based on real data sets is given to see the numerical performance of the proposed basic statistics.

• 응용통계연구
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• 제28권2호
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• pp.361-369
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• 2015

연속측도의 반응변수가 반복측정된 실험 자료의 분석을 위해 흔히 선형혼합모형이 사용된다. 그러나, 잔차의 분포가 이분산성이거나 비정규성을 가질 때 표준적인 선형혼합모형은 적절하지 않은 결과를 가져온다. 잔차의 분포가 두터운 꼬리를 가진 비정규분포를 보이는 타이어 필드시험 데이터를 로버스트 선형혼합모형에 적합시킴으로써 보다 더 정확하고 신뢰할 수 있는 분석결과를 얻을 수 있다. 추가적으로 신뢰성 분석 결과를 제시한다.

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.225-233
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• 1996

In this paper, we propose Bayes estimators of the finite population mean based on heavy-tailed prior distributions using scale mixtures of normals. Also, the asymptotic optimality property of the proposed Bayes estimators is proved. A numerical example is provided to illustrate the results.

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.253-261
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• 1996

In this paper, we derive the maximum likelihood estimator of P=P(X

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.267-280
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• 2007

In this paper, we consider the robust estimation for diffusion processes when the sample is observed discretely. As a robust estimator, we consider the minimizing density power divergence estimator (MDPDE) proposed by Basu et al. (1998). It is shown that the MDPDE for diffusion process is weakly consistent. A simulation study demonstrates the robustness of the MDPDE.

• Journal of the Korean Statistical Society
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• 제27권1호
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• pp.91-100
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• 1998

A method of estimating probability density using regression tools is presented here. It is based on equal-length binning and locally weighted approximate likelihood for bin counts. The method is particularly useful for densities with bounded supports, where it automatically corrects edge effects without using boundary kernels.