The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Kyung H. Seoq - One of the best experts on this subject based on the ideXlab platform.
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An empirical investigation of the shifted Power Transformation method in density estimation
Computational Statistics & Data Analysis, 1992Co-Authors: Byeong U. Park, Sung S. Chung, Kyung H. SeoqAbstract:Abstract Performance of density estimators based on the shifted Power Transformation is investigated through a simulation study. Comparisons to the untransformed density estimators are made for several right skewed population densities. It is observed that the method works quite well in general, as is expected, by reducing mean integrated error, but it is pointed out that a preliminary shift Transformation is necessary for the method to take its full advantage.
Victor M. Guerrero - One of the best experts on this subject based on the ideXlab platform.
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a generalized measure of dispersion
Statistics & Probability Letters, 2020Co-Authors: Victor M. Guerrero, Claudia SolislemusAbstract:Abstract A new measure of dispersion is presented here that generalizes entropy for positive data. It is intrinsically linked to a measure of central tendency and is determined by the data through a Power Transformation that best symmetrizes the observations.
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Variance stabilizing Power Transformation for time series
Journal of Modern Applied Statistical Methods, 2004Co-Authors: Victor M. Guerrero, Rafael PereraAbstract:A confidence interval was derived for the index of a Power Transformation that stabilizes the variance of a time-series. The process starts from a model-independent procedure that minimizes a coefficient of variation to yield a point estimate of the Transformation index. The confidence coefficient of the interval is calibrated through a simulation. Copyright © 2004 JMASM, Inc.
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Selecting a linearizing Power Transformation for time series
Journal of Applied Statistics, 2000Co-Authors: Victor M. GuerreroAbstract:A method is proposed for choosing a Power Transformation that allows a univariate time series to be adequately represented by a straight line, in an exploratory analysis of the data. The method is quite simple and enables the analyst to measure local and global curvature in the data. A description of the pattern followed by the data is obtained as a by-product of the method. A specific form of the coefficient of determination is suggested to discriminate among several combinations of estimates of the index of the Transformation and the slope of the straight line. Some results related to the degree of diff erencing required to make the time series stationary are also exploited. The usefulness of the proposal is illustrated with four empirical applications-two using demographic data and the other two concerning market studies. These examples are provided in line with the spirit of an exploratory analysis, rather than as a complete or confirmatory analysis of the data.
Byeong U. Park - One of the best experts on this subject based on the ideXlab platform.
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An empirical investigation of the shifted Power Transformation method in density estimation
Computational Statistics & Data Analysis, 1992Co-Authors: Byeong U. Park, Sung S. Chung, Kyung H. SeoqAbstract:Abstract Performance of density estimators based on the shifted Power Transformation is investigated through a simulation study. Comparisons to the untransformed density estimators are made for several right skewed population densities. It is observed that the method works quite well in general, as is expected, by reducing mean integrated error, but it is pointed out that a preliminary shift Transformation is necessary for the method to take its full advantage.
Denis Ridley - One of the best experts on this subject based on the ideXlab platform.
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A model-free Power Transformation to homoscedasticity
International Journal of Production Economics, 1994Co-Authors: Denis RidleyAbstract:Abstract Time series modeling explains most of the variation in color television sales over time. However, severe heteroscedasticity makes forecasting sensitive to forecast origin. A distribution context independent method, of regressing the logarithms of the absolute error in fitting a first-order autoregression against the logarithms of the original series is given for exact determination of the exponent of a variance stabilization Power Transformation. The distribution-free (free of the specific forecasting model and the attendant assumption of normality) estimate is compared with the model specific Box-Cox Transformation, and found to have the practical advantages of being direct, automatic, faster and therefore cheaper to implement, and more robust. Some important applications include sales and inventory forecasting for distribution requirements planning in global logistics, forecasting for scheduling in-just-in time operations, and information feed forward for continuous process control.
Sung S. Chung - One of the best experts on this subject based on the ideXlab platform.
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An empirical investigation of the shifted Power Transformation method in density estimation
Computational Statistics & Data Analysis, 1992Co-Authors: Byeong U. Park, Sung S. Chung, Kyung H. SeoqAbstract:Abstract Performance of density estimators based on the shifted Power Transformation is investigated through a simulation study. Comparisons to the untransformed density estimators are made for several right skewed population densities. It is observed that the method works quite well in general, as is expected, by reducing mean integrated error, but it is pointed out that a preliminary shift Transformation is necessary for the method to take its full advantage.
