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New approach in correlation analysis

机译:相关分析的新方法

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In this paper, a new method for restoration of the desired correlations is proposed. It allows to evaluate the “content” of the external factors (l = 1,2,...,L) setting in the form of data arrays y(l) m (x)(m = 1,2,...,M) inside the given Ym (x) function that is supposed to be subjected by the in?uence of these factors. As contrasted to the conventional correlation analysis, the proposed method allows ?nding the “in?uence” functions bl (x)(l = 1,2,...,L) and evaluating the “remnant” array Gm (x) that is remained as a “quasi-independent” part from the in?uence of the factors y(l) m (x). The general expression works as a speci?c “balance” and reproduces the wellknows cases, when bl (x) = Cl (it is reduced to the linear least square method with Gm (x) ~ = 0) and coincides with the remnant function Ym (x) ~ = Gm (x), when the in?uence functions becomes negligible (bl (x) ~ = 0). The available data show that the proposed method allows to extract a small signal from the “pattern” background and it conserves its stability/robustness in the presence of a random ?uctuationsoise. The method is rather ?exible and allows to consider the cases of strong correlations, when the external factors act successively, forming the causeand-e?ect chains. It can be generalized for expressions containing the bonds expressed in the form of memory functions. The proposed method adds new quantitative ties expressed in the form of the desired functions to the conventional correlation relationships expressed in the form of the correlation coe?cients forming, in turn, the correlation matrices. New relationships allow to understand deeper the existing correlations and make them more informative, especially in detection of the desired deterministic and stable bonds/laws that can be hidden inside.
机译:本文提出了一种恢复期望相关性的新方法。它允许以数据数组y(l)m(x)(m = 1,2,...)的形式评估外部因素(l = 1,2,...,L)设置的“内容”。 ,M)在给定的Ym(x)函数中,该函数应该受到这些因素的影响。与常规的相关分析相比,该方法允许查找“影响”函数bl(x)(l = 1,2,...,L)并评估“剩余”数组Gm(x)由于因子y(l)m(x)的影响,α仍然是“准独立”的部分。当bl(x)= Cl(它简化为Gm(x)≤= 0的线性最小二乘法)时,该通用表达式可作为特定的“平衡”并重现已知的情况,并且与剩余函数一致当影响函数可忽略时(bl(x)〜= 0),Ym(x)〜= Gm(x)。现有数据表明,所提出的方法可以从“图案”背景中提取一个小信号,并且在存在随机波动/噪声的情况下可以保持其稳定性/鲁棒性。该方法相当灵活,可以在外部因素相继作用并形成因果链时考虑强相关的情况。对于包含以记忆函数形式表示的键的表达式,可以将其推广。所提出的方法将以期望函数形式表示的新的定量关系添加到以相关系数形式表示的常规相关关系中,从而又形成了相关矩阵。新的关系允许更深入地了解现有的相关性,并使它们更有意义,尤其是在检测可能隐藏在内部的所需确定性和稳定的联系/定律时。

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