In practice, all commonly used heavy-tailed distributions belong to the subexponential class, introduced by Jozef Teugels.
Some authors use the term to refer to those distributions which do not have all their power moments finite; and some others to those distributions that do not have a finite variance.
The definition given in this article is the most general in use, and includes all distributions encompassed by the alternative definitions, as well as those distributions such as log-normal that possess all their power moments, yet which are generally considered to be heavy-tailed.
The distribution of a random variable X with distribution function F is said to have a heavy (right) tail if the moment generating function of X, MX(t), is infinite for all t > 0.
This is also written in terms of the tail distribution function as The distribution of a random variable X with distribution function F is said to have a long right tail[1] if for all t > 0, or equivalently This has the intuitive interpretation for a right-tailed long-tailed distributed quantity that if the long-tailed quantity exceeds some high level, the probability approaches 1 that it will exceed any other higher level.
All long-tailed distributions are heavy-tailed, but the converse is false, and it is possible to construct heavy-tailed distributions that are not long-tailed.
Subexponentiality is defined in terms of convolutions of probability distributions.
For two independent, identically distributed random variables
is defined inductively by the rule: The tail distribution function
on the whole real line is subexponential if the distribution
All commonly used heavy-tailed distributions are subexponential.
Since such a power is always bounded below by the probability density function of an exponential distribution, fat-tailed distributions are always heavy-tailed.
Some distributions, however, have a tail which goes to zero slower than an exponential function (meaning they are heavy-tailed), but faster than a power (meaning they are not fat-tailed).
There are parametric[6] and non-parametric[14] approaches to the problem of the tail-index estimation.
To estimate the tail-index using the parametric approach, some authors employ GEV distribution or Pareto distribution; they may apply the maximum-likelihood estimator (MLE).
a random sequence of independent and same density function
, the Maximum Attraction Domain[15] of the generalized extreme value density
, the maximum domain of attraction of the generalized extreme value distribution
is restricted based on a higher order regular variation property[17] .
[18] Consistency and asymptotic normality extend to a large class of dependent and heterogeneous sequences,[19][20] irrespective of whether
[21][22][23] Note that both Pickand's and Hill's tail-index estimators commonly make use of logarithm of the order statistics.
[24] The ratio estimator (RE-estimator) of the tail-index was introduced by Goldie and Smith.
[25] It is constructed similarly to Hill's estimator but uses a non-random "tuning parameter".
A comparison of Hill-type and RE-type estimators can be found in Novak.
[14] Nonparametric approaches to estimate heavy- and superheavy-tailed probability density functions were given in Markovich.
[27] These are approaches based on variable bandwidth and long-tailed kernel estimators; on the preliminary data transform to a new random variable at finite or infinite intervals, which is more convenient for the estimation and then inverse transform of the obtained density estimate; and "piecing-together approach" which provides a certain parametric model for the tail of the density and a non-parametric model to approximate the mode of the density.
Nonparametric estimators require an appropriate selection of tuning (smoothing) parameters like a bandwidth of kernel estimators and the bin width of the histogram.
The well known data-driven methods of such selection are a cross-validation and its modifications, methods based on the minimization of the mean squared error (MSE) and its asymptotic and their upper bounds.
[28] A discrepancy method which uses well-known nonparametric statistics like Kolmogorov-Smirnov's, von Mises and Anderson-Darling's ones as a metric in the space of distribution functions (dfs) and quantiles of the later statistics as a known uncertainty or a discrepancy value can be found in.
[27] Bootstrap is another tool to find smoothing parameters using approximations of unknown MSE by different schemes of re-samples selection, see e.g.[29]