Gaussian mixture models can be used to estimate such
underlying normal distributions.
Not exact matches
Random processes in nature often
underlie a so - called
normal distribution that enables reliable estimation for the appearance of extreme statistical events.
In the
normal distribution of a bell curve, you never get such extremes, but the pattern
underlying the power curve enables a few rare events of extraordinary magnitude.
Assume that the
underlying asset has a log -
normal return
distribution with mean μ = r − q − σ2 / 2 and variance σ2.
Extreme outliers can spoil the fit, not because of any inherent weakness of the method but because their presence indicates that the
underlying assumption does not hold, i.e. the noise does not follow a
normal distribution — not even approximately.
jkutney — The CLT is useful precisely because the
underlying distribution doesn't have to be
normal.
If some primary studies report an outcome as a dichotomous measure and others use a continuous measure of the same construct, we will convert results for the former from an OR to a SMD, provided that we can assume the
underlying continuous measure has approximately a
normal or logistic
distribution (otherwise we will perform two separate analyses).