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With regards to specific functions of the event time, here we propose to use event time by itself, rank order of the event time and KM estimates.
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The probability density functions of the events, in terms of widths and apparition times, are shown to be governed by Gamma laws and indicate random phenomena; it is observed that the statistical distributions vary with the streamwise position downstream and upstream of the step, the trends being in agreement with the source behavior as evidenced by using the frequency-domain beamforming methods.
In the presence of competing events, cumulative incidence functions of the events of interest are probably evaluated more appropriately by taking into account other events within a competing risk framework.
where denotes the indicator function of the event.
almost surely for all, where is the indicator function of the event and stands for the standard normal distribution function.
Let and be sequences of random variables defined on a fixed probability space and the indicator function of the event.
almost surely for all, here and in the sequel, is the indicator function of the event, and stands for the standard normal distribution function.
For events A and B, (I(A)) denotes the indicator function of the event A, and (I(A,B =I Acap B)).
where, here and in the sequel, I ( A ) is the indicator function of the event A and Φ ( x ) stands for the standard normal distribution function.
Throughout this paper, the symbol denotes a positive constant which is not necessarily the same one in each appearance, and denotes the indicator function of the event.
({mathbf {1}}_{A}) is the indicator function of the event A. For a real-valued number c, let (c^=max{0, c}) and (c^=-min{0,c}).
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CEO of Professional Science Editing for Scientists @ prosciediting.com