• Journal for History of Mathematics
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• v.11 no.2
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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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.E
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• pp.20-32
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• 1990

Abstract This study was conducted to pursue the normalization of frequency distribution by making an approach to the coefficient of skewness to nearly zero through the Box-Cox transformation, to get probable flood flows can be calculated by means of the transformation equation which has been derivated by Box-Cox transformation in the annual maximum series of the applied watersheds. It has been concluded that Box-Cox transfromation is proved to be more efficient than logarithmic, square root and SMEMAX transformation which is based on the trigonometric solution of a right triangle whose three verteces repesent the smallest, median and largest observed values of a population in making the coefficient of skewness nearer to zero. Consequently it is shown that probable flood flows according to the return period based on Box-Cox transformation are closer to the observed data as compared to other methods including SMEMAX transformation and fitted probability distributions such as the three parameter lognormal and the type I extremal distribution for the applied watersheds.

• Journal of the Korean Statistical Society
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• v.34 no.3
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• pp.173-183
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• 2005

The Box-Cox power transformation is generally used for variance stabilization. Recently, Shin and Kang (2001) showed, under the Box-Cox transformation, invariant properties to the original model under the large mean and relatively small variance assumptions in time series analysis. In this paper we obtain some invariant properties in spatial statistics. Spatial statistics, Invariant Property, Variogram, Box-Cox power Transformation.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.137-148
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• 2001

Compositional data found in many practical applications consist of non-negative vectors of proportions with the constraint which the sum of the elements of each vector is unity. It is well-known that the statistical analysis of compositional data suffers from the unit-sum constraint. Moreover, the non-linear pattern frequently displayed by the data does not facilitate the application of the linear multivariate techniques such as principal component analysis. In this paper we develop new type of principal component analysis for compositional data using Box-Cox contrast transformation. Numerical illustrations are provided for comparative purpose.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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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.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.153-159
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• 2003

When available rainfall data is not sufficient, a rough tendency of I-D-F relationship appeared frequently. In fact, rainfall intensity on the curve shows abnormally higher value the longer rainfall duration is applied that gives rise to great confusion to apply a rainfall I-D-F relationships curve to a practical work, however, the research work will present a way to solve above mentioned problem by the use of the Box-Cox transformation formula for a given rainfall data. The study came to a conclusion that the Box-Cox transformation formula is satisfied to utilize in a practical work on the ground of analysis for rainfall data of Sancheong and Yeongcheon.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.413-422
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• 2004

Box-Cox power transformation is employed for analyzing volatilities in Korean financial time series such as KOSPI, KOSDAQ index and interest rates. Statistical procedures for Box-Cox transformed ARCH models are presented. For illustration, diverse financial time series data are analyzed and appropriate power transformations are suggested for each data.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.189-196
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• 2004

The sensitivity of the multivariate Box-Cox transformation model to simultaneous perturbations of all the data based on the likelihood displacement is studied in order to detect outliers. An example is given for illustration.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.967-976
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• 2011

The Box-Cox transformation is a well known family of power transformations 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. Normalization (studentization) of the regressors is a common practice in analyzing microarray data. Here, we implement Box-Cox transformation in normalizing regressors in microarray data. Pridictabilty of the model can be improved using data transformation compared to studentization.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.283-290
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• 2006

Generally speaking, power transformations such as Box-Cox transformation(1964) is applied for variance stabilization and symmetry. But, when the distribution of the original data has a large mean with a small variance or the coefficient of variation is very small, they don't work at all. This paper propose a simple method to introduce a shift parameter before applying power transformations and showed the numerical evidence by Monte Carlo simulation and a real data analysis.