The log normal distribution is a newer approach than power law distributions and gamma distributions.
Not exact matches
Assume that the underlying asset has a
log -
normal return
distribution with mean μ = r − q − σ2 / 2 and variance σ2.
We assigned each of the constraints a
log -
normal distribution from estimates in the literature, as detailed by Allen et al. [11].
If the modern fraction error is normally distributed, then the error
distribution of the RC age is
log normal — since the activity level over time is dictated by the well known «exponential decay» formula and the transform from modern fraction to time is logarithmic.
It appears that the
distribution fits a
log -
normal reasonably well, after I removed approximately 200 deltas that had zero values.
Of course the Levy
distribution is one - sided and has a very heavy right tail, heavier than the
log -
normal or gamma
distributions.
I have found efforts using the
log -
normal distribution and also the gamma
distribution; neither works as well as desired measured over all locations.
Because cortisol data were skewed (skewness statistic ranged from 2.91 to 3.29 for challenge data and from 1.47 to 6.46 for home data, where 0 to 1 represents a
normal distribution and values > 1 represent skewness), we
log - transformed values to normalize the
distributions.