• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.251-260
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• 2002

The aim of this paper is to consider of detecting the location, the jump size and the number of change-points in regression functions by using the local linear fit which is one of nonparametric regression techniques. It is obtained the asymptotic properties of the change points and the jump sizes. and the correspondin grates of convergence for change-point estimators.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.193-199
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• 2005

A simple method is proposed to detect the number of change points with jump discontinuities in nonparamteric regression functions. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Also, the proposed methodology is suggested as the test statistic for detecting of change points and the direction of jump discontinuities.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.31-38
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• 2003

A simple method is proposed to detect the number of change points and test the location and size of multiple change points with jump discontinuities in an otherwise smooth regression model. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Our proposed methodology is explained and applied to real data and simulated data.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.515-524
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• 2007

This paper deals with local linear estimation of fuzzy regression models based on Diamond(1998) as a new class of non-linear fuzzy regression. The purpose of this paper is to introduce a use of smoothing in testing for lack of fit of parametric fuzzy regression models.

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.401-411
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• 2007

In this paper we propose an influence measure for detecting potentially influential observations using the infinitesimal perturbation and the local influence in the log-linear regression model. Also, we propose a goodness-of-fit measure for variable selection. A real data set are used for illustration.

• Journal of the Korean Statistical Society
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• 제5권1호
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• pp.19-28
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• 1976

In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.229-240
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• 2002

Whether to use linear or quadratic model in the analysis of regression data is one of the important problems in classical regression model and measurement error model (MEM). In MEM, four goodness of fit test statistics are available In solving that problem. Two are from the derivation of estimators of quadratic MEM, and one is from that of the general $k^{th}$-order polynomial MEM. The fourth one is derived as a variation of goodness of fit test statistic used in linear MEM. The purpose of this paper is to find the most powerful test statistic among them through the small-scale simulation.

• 한국응용곤충학회지
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• 제26권1호
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• pp.31-36
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• 1987

식물병(植物病) 진전곡선(進展曲線)을 간편하고 융통성있게 기술하는 절편(切片) 1차(次) 회귀(回歸)모델이 본(本) 연구(硏究)에서 제안(提案)되었다. 이 모델은 병진전상황(病進展狀況)을 그 진전형태(進展形態)에 따라 소수(少數)의 1차(次) 회귀식(回歸式)으로 나누고 지표변수(指標變數)를 사용(使用)하여 다시 한개로 묶어 작성(作成)된다. 포장시험(圃場試驗)에서 얻은 12개(個)의 실제병진전상황(實際病進展狀況)에 대(對)한 절편(切片) 1차(次) 회귀(回歸)모델의 통계적(統計的) 적합도(適合度)는 기존(旣存)의 두모델(Logistic모델과 Gompertz모델)에 비(比)하여 증진(增進)되었으며 이 모델이 가진 단순성(單純性), 융통성 및 모수예측(母數豫測)의 용이성(容易性)이 논의(論議)되였다. 그 결과(結果), 절편(切片) 1차(次) 회귀(回歸)모델은 식물병(植物病) 진전(進展)을 기술(記述)하는 한 통계적(統計的) 모델로써 유용(有用)하게 사용(使用)될 수 있으리라 생각된다.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.793-798
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• 2012

In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.

• 대한수학회논문집
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• 제33권3호
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• pp.973-983
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• 2018

Fuzzy linear regression model has been widely studied with many successful applications but there have been only a few studies on the fuzzy regression model with monotonic response function as a generalization of the linear response function. In this paper, we propose the fuzzy regression model with the monotonic response function and the algorithm to construct the proposed model by using ${\alpha}-level$ set of fuzzy number and the resolution identity theorem. To estimate parameters of the proposed model, the least squares (LS) method and the least absolute deviation (LAD) method have been used in this paper. In addition, to evaluate the performance of the proposed model, two performance measures of goodness of fit are introduced. The numerical examples indicate that the fuzzy regression model with the monotonic response function is preferable to the fuzzy linear regression model when the fuzzy data represent the non-linear pattern.