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Null hypothesis zero plane and SCEs (upper and lower surfaces) for C 1 (·, ·) of EEG data with (a) α = 0.05, (b) α = 0.01.

Null hypothesis zero plane and SCEs (upper and lower surfaces) for C 1 (·, ·) of EEG data with (a) α = 0.05, (b) α = 0.01.

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Statistical inference for functional time series is investigated by extending the classic concept of autocovariance function (ACF) to functional ACF (FACF). It is established that for functional moving average (FMA) data, the FMA order can be determined as the highest nonvanishing order of FACF, just as in classic time series analysis. A two-step e...

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... differs significantly from zero (flat surface in Figure 6), indicating strong dependence between η t (·) and η t+1 (·), for more details, see Section 6. ...