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Local decorrelation for error bars in time series
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Doi:
10.20982/tqmp.20.2.p173
Cousineau, Denis
, Proulx, Anthony
, Potvin-Pilon, Annabelle
, Fiset, Daniel
173-185
Keywords:
Error bars
, confidence intervals
, time series
, ERP
, EEG
, fMRI
Tools: R, Matlab, Mathematica
(data file)
 
(Appendix)
Time series and electroencephalographic data are often noisy sources of data. In addition, the samples are often small or medium so that confidence intervals for a given time point taken in isolation may be large. Decorrelation techniques were shown to be adequate and exact for repeated-measure designs where correlation is assumed constant across pairs of measurements. This assumption cannot be assumed in time series and electroencephalographic data where correlations are most-likely vanishing with temporal distance between pairs of points. Herein, we present a decorrelation technique based on an assumption of local correlation. This technique is illustrated with fMRI data from 14 participants and from EEG data from 24 participants.
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