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Surrogate endpoints not uncommonly fail to emulate hard outcomes endpoints and leapfrogging from surrogate data cannot substitute for the exposure of patients in clinical outcome studies.
The emulator facilitates the creation of pluviometric surrogate data and therefore the test of extreme situations.
For the surrogate data analysis, forecast error and time reversal asymmetry statistics are used.
The search for surrogate species or surrogate data for biodiversity is confronted with this complexity.
We employ surrogate data testing and methods of nonlinear time series analysis.
The nature of mood dynamics in bipolar disorder is investigated using surrogate data techniques and nonlinear forecasting.
We then consider the possibility to use the surrogate data in numerical simulations of data-driven models for epidemic spread.
An empirical approach to this analysis is the method of surrogate data ([Lu 2004]; [Kantz and Schreiber 2004]).
For this analysis, the surrogate data must be correlated random numbers with the same power spectrum as the original data.
The value of Γ approaches the value of the Fourier-transformed surrogate data with further increase in U.
The significant results at the group level were obtained by testing against the surrogate data generated on a novel approach.
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