• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.319-331
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• 2022

In this paper, we develop a new statistical model to forecast the PM_2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM_2.5 level well.

• Communications for Statistical Applications and Methods
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• 제5권1호
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• pp.19-24
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• 1998

When multicollinearity appears in linear regression model, we can use ridge regression for stabilizing the regression coefficient estimates. We propose the screen diagram as a graphical method for detecting multicollinearity and estimating ridge constant in linear regression model.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.183-193
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• 2007

This paper shows that a response function for the center of fuzzy output nay not be the same as that for the spread in a fuzzy linear regression model and then suggests a separate fuzzy regression model makes a distinction between response functions of the center and the spread of fuzzy output. Also we use a least squares method to estimate the separate fuzzy regression model and compare an accuracy of proposed model with another fuzzy regression model developed by Diamond (1988) and Kao and Chyu (2003).

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.125-134
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• 2006

This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.541-548
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• 1997

In regression model with nested error structure interval estimations on regression coefficients in different stages are proposed. Ordinary least square estimators and generalized least square estimators of the regression coefficients in this model are derived for between and within group model. The confidence intervals are dervied by using independent idstributional properties between regression coefficient estimators and quadratic froms obtained from the model.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.15-27
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• 2001

One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

• 응용통계연구
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• 제21권6호
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• pp.981-996
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• 2008

서로 다른 출처로부터 얻어진 데이터 파일들을 하나의 데이터 파일로 만드는 통계적 자료결합방법은 공통변수와 서로 다른 고유변수를 포함하여 변수들 간에 존재하는 관련성에 대해 살펴볼 수 있다. Robin (1986)이 제안한 일반회귀모형의 예측값을 이용한 통계적 결합방법은 자료에 대한 다변량 정규성을 가정하기 때문에 이 가정을 위반하는 자료를 이용하는 것은 많은 문제를 수반한다. 본 연구는 제공파일의 고유변수에 모분포를 반영하지 못하는 특이점이 존재하는 경우, 일반회귀모형을 이용한 통계적 결합방법의 대안으로 로러스트 회귀추정방법을 이용한 자료결합방법을 제안하였다. 나아가 로버스트 회귀모형을 이용한 결합방법과 일반회귀모형을 이용한 결합방법에서의 상관관계 및 결정계수 보존에 관한 성능을 비교하기 위하여 모의실험을 수행하였다.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.319-325
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• 2001

In this note, we propose a simple fuzzy linear regression model for fuzzy input-output data based on Tanaka's approach. Then an LP-based method to derived the satisfying solution of the decision making is developed.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.539-556
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• 2019

This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".

• 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.