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A Study on the Comparison of Performances and the Verification of Relationship between Influence Functions on t Statistic and Coefficient of Variation in Big data
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빅데이터에서의 t통계량과 변동계수에 대한 영향함수 성능 비교 및 관계 규명

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Type
Academic journal
Author
Journal
The Korean Data Analysis Society Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제21권 제3호 KCI Accredited Journals
Published
2019.1
Pages
1,275 - 1,284 (10page)

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A Study on the Comparison of Performances and the Verification of Relationship between Influence Functions on t Statistic and Coefficient of Variation in Big data
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Data involving outlier should be carefully analyzed because it can lead to distorted results. But it is not easy to analyze those outliers because big data usually has a characteristic of large scale and short cycle. In this paper, we validate and compare the performances of influence functions on t statistic and coefficient of variation after defining relative difference using influence function that can be used to find outliers in big data. We also induce the empirical influence function relationship between on t statistic and on coefficient of variation from the formula and check through simulation. As a result, the performance (prediction) of influence function on coefficient of variation is better than that on t statistic. And they also have a multiplicative relationship. Empirical influence function on t statistic equates to {-{bar{x}}^{2} sqrt{n}} over {s^{2}} multiplied by empirical influence function on coefficient of variation. And empirical influence function on coefficient of variation is the reciprocal of {-{bar{x}}^{2} sqrt{n}} over {s^{2}} times empirical influence function on t statistic.
#influence function #t statistic #coefficient of variance #performance comparison #relation.

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References (15)

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Campbell, N. A. / 1978 / The influence function as an aid in outlier detection in discrimination analysis / Applied Statistics 27:251~258 google schola 최승배 / 2006 / Sensitivity Analysis in Fitting Auto- and Cross-Variogram Model / Journal of The Korean Data Analysis Society 8(4):1265~1279 dbpia Choi, S. / 2000 / Sensitivity analysis in variogram estimation : detecting observations influential to Cressie and Hawkins(1980)’s robust variogram / The Journal of Korean Data Analysis Society 2(3):359~366 google schola Choi, S. / 2000 / Sensitivity analysis in auto-and cross-variogram estimation / The Journal of Korean Data Analysis Society 2(1):91~107 google schola Cook, R. D. / 1982 / Residual and influence in regression / Chapman and Hall google schola

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