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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Scaled correlation ： ウィキペディア英語版 Scaled correlation In statistics, scaled correlation is a form of a coefficient of correlation applicable to data that have a temporal component such as time series. If the signals have multiple components (slow and fast), scaled coefficient of correlation can be computed only for the fast components of the signals, ignoring the contributions of the slow components.〔Nikolić D, Muresan RC, Feng W, Singer W (2012) Scaled correlation analysis: a better way to compute a cross-correlogram. ''European Journal of Neuroscience'', pp. 1–21, doi:10.1111/j.1460-9568.2011.07987.x http://www.danko-nikolic.com/wp-content/uploads/2012/03/Scaled-correlation-analysis.pdf〕 This filtering-like operation has the advantages of not having to make assumptions about the sinusoidal nature of the signals. For example, in the studies of brain signals researchers are often interested in the high-frequency components (beta and gamma range; 25–80 Hz), and may not be interested in lower frequency ranges (alpha, theta, etc.). In that case scaled correlation can be computed only for frequencies higher than 25 Hz by choosing the scale of the analysis, ''s'', to correspond to the period of that frequency (e.g., ''s'' = 40 ms for 25 Hz oscillation). ==Definition== Scaled correlation between two signals is defined as the average correlation computed across short segments of those signals. First, it is necessary to determine the number of segments $K$ that can fit into the total length of the signals $T$ for a given scale $s$: :$K\; =\; \backslash operatorname\backslash left(\backslash frac\backslash right).$ Next, if $r\_k$ is Pearson's coefficient of correlation for segment $k$, the scaled correlation across the entire signals $\backslash bar\_s$ is computed as :$\backslash bar\_s\; =\; \backslash frac\; \backslash sum\backslash limits\_^K\; r\_k.$ 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Scaled correlation」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.