Search results
Results from the WOW.Com Content Network
Distributions with zero excess kurtosis are called mesokurtic, or mesokurtotic. The most prominent example of a mesokurtic distribution is the normal distribution family, regardless of the values of its parameters .
The probability density function for logarithm of weekly record sales changes is highly leptokurtic and characterized by a narrower and larger maximum, and by a fatter tail than in the normal distribution case. On the other hand, this distribution has only one fat tail associated with an increase in sales due to promotion of the new records ...
to find out if its mesokurtic, platykurtic or leptokurtic, why compare it to 3? —Preceding unsigned comment added by Reesete (talk • contribs) 10:18, 5 March 2008 (UTC) The expected Kurtosis for sample of IID standard normal data is 3 (see the wiki article on the normal distribution for more).
The shape of a distribution will fall somewhere in a continuum where a flat distribution might be considered central and where types of departure from this include: mounded (or unimodal), U-shaped, J-shaped, reverse-J shaped and multi-modal. [1]
However, the phase-type is a light-tailed or platykurtic distribution. So the representation of heavy-tailed or leptokurtic distribution by phase type is an approximation, even if the precision of the approximation can be as good as we want.
GAMLSS is especially suited for modelling a leptokurtic or platykurtic and/or positively or negatively skewed response variable. For count type response variable data it deals with over-dispersion by using proper over-dispersed discrete distributions.
move to sidebar hide. From Wikipedia, the free encyclopedia
It is a useful way to parametrize a continuum of symmetric, platykurtic densities spanning from the normal (=) to the uniform density (=), and a continuum of symmetric, leptokurtic densities spanning from the Laplace (=) to the normal density (=).