Analysis of consistence level using new method of statistical transformation approach in multi-spectral fluctuation pattern

Melinda, A. Tamsir, Basari, D. Gunawan
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引用次数: 6

Abstract

In this study, quite new method of statistical approach which is known as TST (tamsir statistical transformation) is introduced. TST is applied in order to obtain a consistence level of multi-spectral fluctuation pattern of HHF (high high-frequency). This is done because the data from the measurement results have large amount of data and have not been consistent yet. Therefore, it is essential to have quite good method to treat the data that can bridge the data processing previously. There are several parameters of the data analysis, which will be discussed, such as: the value of Total-C (total value comparison), consistence of fluctuation (CF), consistence of variance to mean ratio (C-VMR) and consistence of value (CV). Besides, the data are broken down into several groups of data. This is done because it is fruitful to seek the best group that has preferable consistence compare to others. The results obtained show that the statistical approach can determine the consistent results of data grouping for large data size. Moreover, the new approach of TST can accommodate to compute the consistence level of multi-spectral fluctuation pattern.
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基于统计变换的多光谱波动模式一致性分析
本文介绍了一种新的统计方法,即坦西尔统计变换。采用TST是为了获得高频段多光谱波动模式的一致性水平。这样做是因为来自测量结果的数据量很大,而且还不一致。因此,有一个比较好的方法来处理以前的数据处理是很重要的。数据分析有几个参数,将讨论,如:total - c(总价值比较)的值,波动一致性(CF),方差均值比的一致性(C-VMR)和值的一致性(CV)。此外,数据被分成几组数据。这样做是因为寻找最好的团队比其他团队具有更好的一致性是富有成效的。结果表明,统计方法可以确定大数据量下数据分组结果的一致性。此外,新的TST方法可以计算多光谱波动模式的一致性水平。
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