What is a Pareto random variable?

What is a Pareto random variable?

Definitions. If X is a random variable with a Pareto (Type I) distribution, then the probability that X is greater than some number x, i.e. the survival function (also called tail function), is given by. where xm is the (necessarily positive) minimum possible value of X, and α is a positive parameter.

What kind of distribution is Pareto?

continuous power law distribution
The Pareto distribution is a continuous power law distribution that is based on the observations that Pareto made. The pdf for it is given by f ( x ) = α x α + 1 and the cdf is given by F ( x ) = 1 − 1 x α . The expected value of the function is based on the parameter.

What is the name of the transformed Pareto distribution?

When raising to the power, the resulting distribution is an inverse transformed Pareto distribution and it is also called an inverse Burr distribution. When raising to the power -1, the resulting distribution is an inverse Pareto distribution (it does not have a special name other than inverse Pareto).

How is the Pareto distribution different from Zipf’s law?

The Pareto distribution is a continuous probability distribution. Zipf’s law, also sometimes called the zeta distribution, is a discrete distribution, separating the values into a simple ranking. Both are a simple power law with a negative exponent, scaled so that their cumulative distributions equal 1.

When is a conditional probability distribution a Pareto distribution?

Conditional distributions. The conditional probability distribution of a Pareto-distributed random variable, given the event that it is greater than or equal to a particular number exceeding , is a Pareto distribution with the same Pareto index but with minimum instead of .

How is the Pareto distribution used in actuarial modeling?

The Pareto distribution itself can be generated as a mixture of exponential distributions with gamma mixing weight (see here ). Thus from basic building blocks (exponential and gamma), vast families of distributions can be created, thus expanding the toolkit for modeling.