• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.687-696
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• 2013

The detection and the examination of outliers are important parts of data analysis because some outliers in the data may have a detrimental effect on statistical analysis. Outlier detection methods have been discussed by many authors. In this article, we propose to apply Hadi and Simonoff's (1993) method to penalized spline a regression model to detect multiple outliers. Simulated data sets and real data sets are used to illustrate and compare the proposed procedure to a penalized spline regression and a robust penalized spline regression.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.285-288
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• 2003

신용위험 관리에서 필수적인 방법론이 스코어 카드이며 이를 작성하는 데에 있어서 널리 쓰이는 방법 중의 하나가 로지스틱 회귀분석이다. 본 논문에서는 로지스틱 회귀 방법에 기초한 스플라인 방법론을 소개하고자 한다. 최종 스코어 카드는 연속형 변수를 범주형 변수화 하므로 조각 선형 스플라인을 채택하였다. 모의 실험을 통하여 제안된 방법의 성 능을 규명 하였다.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.371-381
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• 2000

In this paper, we describes smoothing spline in nonparametric regression and some asymptotic results for estimates of the regression cofficients in the parametric part were biased on semi parametric regression estimator.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.171-178
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• 1991

For the polynomial spline regression with fixed knots, some properties of the D-optimal design are discussed. Also the D-optimal design for some cases are found analytically by using a normalized B-spline basis for $S(P_m : k : \Delta)$. Based on the Kiefer-Wolfowitz equivalence theorem, the D-optimal design for some cases are found by numerical methods.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.181-192
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• 1992

We consider the estimation of the regression surface in generalized linear models based on tensor-product B-splines in a data-dependent way. Our approach is to use maximum likelihood method to estimate the regression function by a function from a space of tensor-product B-splines that have a finite number of knots and are linear in the tails. The knots are placed at selected order statistics of each coordinate of the sample data. The number of knots is determined by minimizing a variant of AIC. A numerical example is used to illustrate the performance of the tensor spline estimates.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1047-1058
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• 2008

Prediction of total sales in information security industry is considered. Exponential smoothing and spline smoothing is applied to the time series of annual sales data. Due to the different survey items of every year, we recollect the original survey data by some basic criterion and predict the sales to 2014. We show the total sales in infonnation security industry are increasing gradually by year.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.199-203
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• 2008

수위관측지점의 수위-유량관계는 하천단면 형태와 경사에 따라 1개의 유량곡선이 형성될 수도 있으나, 저수위, 평수위, 고수위 등 여러 개 구간의 곡선이 각각 다른 형태로 구성될 수 있다. 또한 이러한 곡선의 연결접점에서 연결이 연속적이어야 하고 부드러워야 한다고 알려져 있다. 각 수위구간에서 유도된 수위-유량관계곡선이 수위구간을 나눈 경계수위에서 곡선식이 일치하지는 경우가 종종 나타난다. 본 연구에서는 경계수위와 곡선접점이 일치하는지 여부를 판단하고 오차의 크기를 확인하기 위하여 할선법을 적용하여 Excel의 VBA로 프로그램을 작성하여 사용하였다. 한편, 수위유량곡선의 연결부에서 부드럽게 연결되는 것이 바람직 하지만, 지금까지 실무에서는 경계수위에서 기울기가 급변하는 형태의 수위-유량관계곡선을 사용하고 있다. 왜냐하면 최소자승회귀분석을 통하여 유도되는 과정에서 경계수위지점의 1차 도함수를 일치시키는 것은 현실적으로 불가능하기 때문이다. 본 연구에서는 이러한 문제를 해결하기 위하여, 회귀분석으로 지수함수의 형태로 유도된 수위-유량관계곡선으로부터 수위와 유량의 대표 값을 계산하고 이들 대표 값들을 3차 스플라인 보간법을 이용하여 1차 도함수를 접점에서 일치시키는 부드러운 연결과 함께 수위-유량관계곡선과도 잘 일치하는 새로운 대안을 제시하였다. 2006년도 한국수문조사연보(유량편)의 자료를 검토하여 문제점들을 도출하고, 본 연구에서 제안한 방법들을 적용하여 보았다.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.29-36
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• 1998

A Simple, closed form expression is derived for a linear smoothing spline by Eubank (1994, 1997). We introduced his estimater and studied how well examples are fitted to this estimator with the values of minimum generalized cross validation.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.