• Journal of the Korean Data and Information Science Society
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• 제12권1호
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• pp.103-125
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• 2001

The Box-Cox transformation is a well known family of power transformation that brings a set of data into agreement with the normality assumption of the residuals and hence the response variable of a postulated model in regression analysis. This paper proposes a new method for estimating the Box-Cox transformation using maximization of the Sample Entropy statistic which forces the data to get closer to normal as much as possible. A comparative study of the proposed procedure with the maximum likelihood procedure, the procedure via artificial regression estimation, and the recently introduced maximization of the Shapiro-Francia W' statistic procedure is given. In addition, we generate a table for the optimal spacings parameter in computing the Sample Entropy statistic.

• 정보처리학회논문지A
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• 제9A권1호
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• pp.37-44
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• 2002

기존의 순차 프로그램에서 병렬성을 추출하는 연구들은 하나의 프로시저 내 변환에 치중되고 있다. 그러나 대부분의 프로그램들은 프로시저간 잠재된 병렬성을 가지고 있다. 본 논문에서는 자료 종속성 제거방법을 이용하여 프로시저 호출을 가진 루프에서 병렬성 추출 방식을 제안한다. 프로시저 호출을 포함하는 루프의 병렬화는 대부분 자료종석거리가 uniform 형태의 코드에서만 연구되었다. 본 논문에서는 자료종속거리가 uniform 코드와 nonuniform 코드에 대해 모두 적용 가능한 프로시저 간 변환 방법을 제시하였으며, 제시된 알고리즘의 성능평가를 위하여 CRAY T3E에서 성능평가하였고, 제시된 방법이 효과적임을 보였다.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.463-471
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• 2019

The variable transformations are useful ways to guarantee the functional relationships in the model. However, the presence of outliers may undermine the accuracy of transformation. This paper deals with response transformations in the partial linear models under the existence of outliers. A new procedure for response transformation and outliers detection is proposed. The procedure uses a sequential method for identifying outliers and dynamic graphical methods for an appropriate transformation. The graphical tools make it possible to catch diagnostic information by monitoring the movement of points in the data. The procedure is illustrated with several examples. Examples show that visual clues regarding the optimal transformation, the fittness of the model and the outlyness of the observations can be checked from the series of plots.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.574-574
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.575-583
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Journal of applied mathematics & informatics
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• 제22권1_2호
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• pp.541-547
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• 2006

A procedure for investigating the influence of observations in choosing Box-Cox transformation for multivariate data is suggested. It is effective in spotting influential observations. A numerical example is provided for illustration.

• 응용통계연구
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• 제33권1호
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• pp.1-10
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• 2020

본 연구에서는 선형회귀모형에서 이상치와 변수변환을 고려한 변수선택 알고리즘을 다룬다. 제안된 방법은 잠재적 이상치를 탐지하여 제거한 후 변수변환 추정을 위해 최소 절사 제곱 추정법을 적용하며 가능한 모든 회귀모형을 비교하여 최종적으로 변수를 선택한다. 정확한 변수 선택과 추정된 모델의 적합도의 맥락에서 방법의 효율성을 보여주기 위해 실제 데이터 분석 및 시뮬레이션 결과가 제시된다.

• Elastomers and Composites
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• 제53권2호
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• pp.52-56
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• 2018

The present study examines the transformation of dynamic DSC data into the equivalent isothermal data for the formation kinetics of a polyurethane elastomer. The reaction of 2'-dichloro-4,4'-methylenedianiline (MOCA) with a PTMG/TDI-based isocyanate prepolymer was evaluated. DSC measurement was performed in the dynamic scanning mode with several different heating rates to obtain the reaction thermograms. Then, the data was transformed into the isothermal data through a procedure based on Ozawa analysis. The main feature of this procedure was the transformation of $({\alpha}-T)_{\beta}$ curves from dynamic DSC into $({\alpha}-t)_T$ curves using the isoconversional $(t-T)_{\alpha}$ diagram. Validity was discussed for the relationship between the dynamic DSC data and the transformed isothermal results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 학술대회 논문집 전문대학교육위원
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• pp.7-12
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• 2001

A large number of methods for the design optimization have been proposed in recent years. However, it is not easy to apply these methods to practical use because of many iterations. So, in the design optimization, physical and engineering investigation of the given model are very important, which results in an overall increase in the optimization speed. This paper describes a novel optimization procedure utilizing the conformal transformation method. This approach consists of two phases and has the advantage of grasping the physical phenomena of the model easily. Some numerical results that demonstrate the validity of the proposed method are also presented.

• 한국수학사학회지
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• 제11권2호
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• pp.63-76
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• 1998

When teaching the linear regression analysis in the class, the power transformation must be introduced to fit the linear regression model for nonlinear data. Box and Cox (1964) proposed the attractive power transformation technique which is so called Box-Cox transformation. In this paper, an effective algorithm selecting an appropriate value for Box-Cox transformation is developed which is considered to find a value minimizing error sum of squares. When the proposed algorithm is used to find a value for transformation, the number of iterations needs to be considered. Thus, the number of iterations is examined through simulation study. Since SAS is one of most widely used packages and does not provide the procedure that performs iterative Box-Cox transformation, a SAS program automatically choosing the value for transformation is developed. Hence, the students could learn how the Box-Cox transformation works, moreover, researchers can use this for analysis of data.