The shape of empirical distributions with heavy tails is a recurrent matter of debate. There are claims of a power laws and the associated scale invariance. There are plenty of challengers as well, the lognormal and stretched exponential among others. Here I point out that, with regard to summation invariance, all what matters is they are subexponential distributions. I provide numerical examples highlighting the key properties of subexponential distributions. The summation invariance and the black swan dominance: the sum is dominated by the maximum. Finally, I illustrate the use of these properties to tackle problems in random networks, infectious dynamics and project delays.Comment: 6 pages, 3 figure
Perhaps the most recent controversial topic in network science research is to determine whetherreal-...
Long-tailed distributions are common in natural and engineered systems; as a result, we encounter ex...
The concept of heavy- or long-tailed densities (or distributions) has attracted much well-deserved a...
AbstractIt is known that large deviations of sums of subexponential random variables are most likely...
In a recently published paper in Physica A it is claimed the statistical evidence of having a power ...
This text studies heavy-tailed distributions in probability theory, and especially convolutions of s...
This chapter is devoted to the parametric statistical distributions of economic size phenomena of va...
We develop a simple test for deviations from power law tails, which is based on the asymptotic prope...
We study conditions under which P{Sτ > x} ∼ P{Mτ > x} ∼ EτP{ξ1 > x} as x → ∞, where Sτ is a...
Although understanding tail behavior of distributions is important in many areas, such as telecommun...
AbstractLet Sn, n ⩾ 1, be the partial sums of i.i.d. random variables with negative mean value. Many...
We give a sufficient condition for i.i.d. random variablesX1,X2 in order to have P{X1-X2>x} ~ P{|X1|...
Power law distributions, also known as heavy tail distributions, model distinct real life phenomena...
The tail behavior of aggregates of heavy-tailed random vectors is known to be determined by the so-c...
Heavy-tails are a continual source of excitement and confusion across disciplines as they are repeat...
Perhaps the most recent controversial topic in network science research is to determine whetherreal-...
Long-tailed distributions are common in natural and engineered systems; as a result, we encounter ex...
The concept of heavy- or long-tailed densities (or distributions) has attracted much well-deserved a...
AbstractIt is known that large deviations of sums of subexponential random variables are most likely...
In a recently published paper in Physica A it is claimed the statistical evidence of having a power ...
This text studies heavy-tailed distributions in probability theory, and especially convolutions of s...
This chapter is devoted to the parametric statistical distributions of economic size phenomena of va...
We develop a simple test for deviations from power law tails, which is based on the asymptotic prope...
We study conditions under which P{Sτ > x} ∼ P{Mτ > x} ∼ EτP{ξ1 > x} as x → ∞, where Sτ is a...
Although understanding tail behavior of distributions is important in many areas, such as telecommun...
AbstractLet Sn, n ⩾ 1, be the partial sums of i.i.d. random variables with negative mean value. Many...
We give a sufficient condition for i.i.d. random variablesX1,X2 in order to have P{X1-X2>x} ~ P{|X1|...
Power law distributions, also known as heavy tail distributions, model distinct real life phenomena...
The tail behavior of aggregates of heavy-tailed random vectors is known to be determined by the so-c...
Heavy-tails are a continual source of excitement and confusion across disciplines as they are repeat...
Perhaps the most recent controversial topic in network science research is to determine whetherreal-...
Long-tailed distributions are common in natural and engineered systems; as a result, we encounter ex...
The concept of heavy- or long-tailed densities (or distributions) has attracted much well-deserved a...