2010
DOI: 10.1002/9781118032428
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Cited by 1,241 publications
(388 citation statements)
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“…Bendat and Piersol (1986) have shown that the expectation values of the cross-correlation function will show a peak around the mean transit time t 0 for nondispersive advection. The actually calculated cross-correlation, as a stochastic estimator, will also contain statistic uncertainties, including an uncertainty in the actual value of t 0 .…”
Section: Inter-annual To Multi-decadal Changes Of Lsw In the Irmingermentioning
confidence: 99%
“…Bendat and Piersol (1986) have shown that the expectation values of the cross-correlation function will show a peak around the mean transit time t 0 for nondispersive advection. The actually calculated cross-correlation, as a stochastic estimator, will also contain statistic uncertainties, including an uncertainty in the actual value of t 0 .…”
Section: Inter-annual To Multi-decadal Changes Of Lsw In the Irmingermentioning
confidence: 99%
“…To be strictly stationary, all moments of the data must be shown to be constant [23]. To reject the assumption of stationarity, it is sufficient to show that the joint probability density function, f(x,y), varies appreciably over the interval.…”
Section: A Measure Of Stationaritymentioning
confidence: 99%
“…Analyses which require stationarity may still be performed with some degree of confidence if the data can be shown to be weakly stationary. Bendat and Piersol [23] suggest a test for weak stationarity in which a set of statistics are taken from the data for several subintervals, as was done for the joint density function, above. The set are then tested for trends using a 'reverse arrangements' hypothesis test in which the number of transitions from 'less than' to 'greater than' are counted in the data.…”
Section: A Measure Of Stationaritymentioning
confidence: 99%
“…5(b) shows the normalized probability distribution functions for τ = 0.1 s using 8 × 10 5 samples. The shape of the PDFs correspond to a Gaussian distribution, as expected for large sample sizes according to the Central Limit Theorem (Bendat & Piersol 1986). Such a normal distribution can clearly be distinguished from other kinds of distribution functions (e.g.…”
Section: T H E T R a N S I T I O N O F T H E S C A L I N G B E H Av Imentioning
confidence: 55%