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We suppose the random variables being considered are defined on a probability space ((Omega,mathcal {F},P)) for a base probability P. In order to explain comonotone additivity, we first note that two random variables X,Y are said to be comonotone if, for example, one is a monotone increasing function of the other.
Here, Ω ⊂ R n Open image in new window is a bounded domain with a sufficiently smooth boundary, ∥·∥2 is the norm in L2, and the nonlinearities considered are defined by: M ( s 2 ) = α + β s 2 γ, α ≥ 0, β ≥ 0, α + β > 0, γ ≥ 1, Open image in new window (2) g ( u t ) = δ u t | u t | λ − 2, δ > 0, λ ≥ 2, Open image in new window (3).
The variables considered are defined in Table 1.
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The skill groups considered were defined by sex, race and educational level.
The range in which data points are considered is defined by the smoothing factor.
To limit the number of genes considered, for each QTL the interval considered was defined by the two SNP markers flanking the QTL peak.
The six measures of time we consider are defined in Table 1, both in the demographic sense we describe, as well as in a more general event history interpretation.
Therefore, it is interesting to find some conditions under which the algorithms considered are well defined.
where and for the considered cases are defined as in (19), (22), and (24), respectively.
The two considered levels are defined by the intervals [ −6,6] and [ −24,−12] measured in semitones.
Four plausible pre-emptive vaccination strategies were considered and are defined in Table 1.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com