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Choi, Jaesung
- Journal of the Korean Data and Information Science Society
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- v.24 no.2
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- pp.333-339
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- 2013
This paper discusses a method for getting a basis set of estimable functions of less than full rank linear model. Since model parameters are not estimable estimable functions should be identified for making inferences proper about them. So, it suggests a method of using full rank factorization of model matrix to find estimable functions in easy way. Although they might be obtained in many different ways of using model matrix, the suggested full rank factorization technique could be one of much easier methods. It also discusses how to use projection matrix to identify estimable functions.
https://doi.org/10.7465/jkdi.2013.24.2.333 인용 PDF KSCI

Lee, Jang Taek
- Journal of the Korean Data and Information Science Society
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- v.28 no.2
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- pp.309-316
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- 2017
In baseball, estimation of winning percentage is critical and many studies for this topic have been actively performed. Pairwise winning percentage estimation using Pythagorean winning percentages of individual teams against other individual teams has the property that the sum of estimated winning percentage totals must be a constant. In this paper, we consider two types of pairwise estimation including linear formula and Pythagorean formula to the Korean baseball data of seasons from 2013 to 2016 under the criterions of RMSE and MAD. In conclusion, pairwise Pythagorean methods have the smaller RMSE and MAD than traditional Pythagorean methods. We suggest the optimal pairwise Pythagorean formula with a fixed exponent. Also we show that there are very little differences of RMSE and MAD between variation in exponent values.
https://doi.org/10.7465/jkdi.2017.28.2.309 인용 PDF KSCI