• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.387-394
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• 2007

This paper considers several stratification strategies for multivariate and multipurpose survey with several quantitative stratification variables. We propose three methods of stratification based on, respectively, the method of cumulative frequency square root which is the most popular one in univariate stratification, cluster analysis, and factor analysis followed by cluster analysis. We then compare the efficiency of those methods using the Dong-Eup-Myun data of the holding numbers of farming machines, extracted from the 2001 Agricultural Census. It turned out that the method based on cluster analysis with factor analysis would be a relatively satisfactory strategy.

• Journal of the Korean Solar Energy Society
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• v.38 no.5
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• pp.11-25
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• 2018

The Ln-least method is commonly used to estimate the Weibull parameters from the observed wind speed data. In previous studies, the bin method has been used to calculate the cumulative frequency distribution for the Ln-least method. The purpose of this study is to obtain better performance in the Ln-least method by applying probability plotting position(PPP) instead of the bin method. Two types of the wind speed data were used for the analysis. One was the observed wind speed data taken from three sites with different topographical conditions. The other was the virtual wind speed data which were statistically generated by a random variable with known Weibull parameters. Also, ten types of PPP formulas were applied which were Hazen, California, Weibull, Blom, Gringorten, Chegodayev, Cunnane, Tukey, Beard and Median. In addition, in order to suggest the most suitable PPP formula for estimating Weibull parameters, two accuracy tests, the root mean square error(RMSE) and $R^2$ tests, were performed. As a result, all of PPPs showed better performances than the bin method and the best PPP was the Hazen formula. In the RMSE test, compared with the bin method, the Hazen formula increased estimation performance by 38.2% for the observed wind speed data and by 37.0% for the virtual wind speed data. For the $R^2$ test, the Hazen formula improved the performance by 1.2% and 2.7%, respectively. In addition, the performance of the PPP depended on the frequency of low wind speeds and wind speed variability.