• 대한수학회논문집
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• 제31권1호
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• pp.185-198
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• 2016

Regression analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper propose a fuzzy regression analysis applying Theils method which is not sensitive to outliers. This method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. An example and two simulation results are given to show fuzzy Theils estimator is more robust than the fuzzy least squares estimator.

• 품질경영학회지
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• 제21권2호
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• pp.162-169
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• 1993

For the problem of testing the parallelism of several regression lines against ordered alternatives, two test statistics and proposed and examined. The proposed statistics are linear combinations of robust estimators of slope parameters, which are modifications of the Adichie (1976) test based on scores. The asymptotic null variances of the proposed states tics are estimated by the kernel density estimation methods. The proposed tests are compared with the Adichie's test in terms of asymptotic relative efficiency and small-sample powers.

• 응용통계연구
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• 제20권2호
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• pp.313-322
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• 2007

오염이 있는 생존자료에서 여러 가지 회귀뎁스(regression depth)를 비교 연구하였다. 중도절단 자료에서 회귀뎁스에 대한 정의는 Park과 Hwang(2003)의 반공간회귀뎁스(halfspace regression depth)와 Park(2003)의 심플리셜 회귀뎁스(simplicial regression depth)가 있다. 본 논문은 Hubert 등(2001)이 제안한 사영회귀뎁스(projection regression depth)를 생존자료에서 사용하는 방법을 제시하고 이 방법과 기존의 뎁스기반 회귀모형과의 비교를 다양한 오염 상황에서 실시하였다.

• Communications for Statistical Applications and Methods
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• 제21권3호
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• pp.261-274
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• 2014

In this paper we develop Bayes and empirical Bayes estimators of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation under the balanced loss function. We compare the performance of the optimal Bayes estimator with ones of the classical sample mean and the usual Bayes estimator under the squared error loss with respect to the posterior expected losses, risks and Bayes risks when the underlying distribution is normal as well as when they are binomial and Poisson.

• 한국멀티미디어학회논문지
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• 제12권1호
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• pp.26-30
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• 2009

본 논문에서는 로버스트 통계학의 순위 추정을 기반으로 하고 순서통계학의 L-추정자를 이용한 적응 순환 최소 순위(RLR) L-필터를 제안한다. 제안한 RLR-L 필터는 비선형 적응알고리즘을 가진 비선형 적응 필터로서 오차의 분산측정을 최소화하는 관점의 최적 필터로 가변적인 스텝 크기를 가지며 적응한다. 제안한 필터는 영상신호와 같은 비정체 신호나 가우시안 잡음 또는 임펄스 잡음과 같은 비선형 채널에 적합하다.

• 대한수학회논문집
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• 제20권1호
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• pp.145-159
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• 2005

In this paper we consider the problem of testing statistical hypotheses for unknown parameters in nonlinear regression models and propose three asymptotically equivalent tests based on regression quantiles estimators, which are Wald test, Lagrange Multiplier test and Likelihood Ratio test. We also derive the asymptotic distributions of the three test statistics both under the null hypotheses and under a sequence of local alternatives and verify that the asymptotic relative efficiency of the proposed test statistics with classical test based on least squares depends on the error distributions of the regression models. We give some examples to illustrate that the test based on the regression quantiles estimators performs better than the test based on the least squares estimators of the least absolute deviation estimators when the disturbance has asymmetric and heavy-tailed distribution.

• Communications for Statistical Applications and Methods
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• 제1권1호
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• pp.27-32
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• 1994

We propose a robust regression estimator which has both a high breakdown point and a bounded influence function. The main contribution of this article is to present a weight function in the generalized M (GM)-estimator. The weighting schemes which control leverage points only without considering residuals cannot be efficient, since control leverage points only without considering residuals cannot be efficient, since these schemes inevitably downweight some good leverage points. In this paper we propose a weight function which depends both on design points and residuals, so as not to downweight good leverage points. Some motivating illustrations are also given.

• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.97-113
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• 2017

This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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• pp.63-76
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• 2006

유전자 발현 자료(gene expression data)를 분석하기 위한 여러 가지 군집 알고리즘(clustering algorithm)과 군집 결과들을 검증하는 척도, 즉 군집 타당성분석 기법(cluster validation technique)이 제안되고 있지만, 이틀 군집 타당성을 분석하는 기법들에 대한 성능의 비교 평가는 매우 드물다. 본 논문에서는 모의 생성 자료로 몇 가지 특정 상황을 연출하여 군집 타당성 분석 기법들을 비교해 보고, 실제 유전자 발현 자료 두 가지에 대해서도 이들 기법의 성능을 비교 평가해 보았다.

• 전기학회논문지
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• 제61권11호
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• pp.1682-1688
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• 2012

This paper introduces a GP(Genetic Programming) based robust technique for temperature compensation in short-range prediction. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, because forecast models do not reliably determine weather conditions. Most of MOS use a linear regression to compensate a prediction model, therefore it is hard to manage an irregular nature of prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP is suggested. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days temperatures in Korean regions. This method is then compared to the UM model and has shown superior results. The training period of 2007-2009 summer is used, and the data of 2010 summer is adopted for verification.