A probability distribution which, for any may be represented as a composition (convolution) of identical probability distributions. The definition of an infinitely-divisible distribution is applicable to an equal degree to a distribution on the straight line, on a finite-dimensional Euclidean space and to a number of other, even more general, cases. The one-dimensional case will be considered below.
The characteristic function of an infinitely-divisible distribution is called infinitely divisible. Such a function may be represented, for any value of , as the -th power of some other characteristic function:
Examples of infinitely-divisible distributions include the normal distribution, the Poisson distribution, the Cauchy distribution, and the "chi-squared" distribution. The property of infinite divisibility is most easily tested by using characteristic functions. The composition of infinitely-divisible distributions and the limit of weakly-convergent sequences of infinitely-divisible distributions are again infinitely divisible.
A random variable, defined on some probability space, is called infinitely divisible if it can be represented, for any , as a sum of independent identically-distributed random variables defined on that space. The distribution of each such variable is infinitely divisible, but the converse is not always true. Consider, e.g., the discrete probability space formed by with Poisson probabilities
The random variable is not infinitely divisible, even though its probability distribution (Poisson distribution) is infinitely divisible.
Infinitely-divisible distributions first appeared in connection with the study of stochastically-continuous homogeneous stochastic processes with stationary independent increments (cf. Stochastic process with stationary increments; Stochastic process with independent increments) [Fin], [Ko], [L]. This is the name of processes , , which satisfy the following requirements: 1) ; 2) the probability distribution of the increment , , depends only on ; 3) for the differences
are mutually-independent random variables; 4) for any ,
as . For such a process the value for any will be an infinitely-divisible random variable, and the corresponding characteristic function satisfies the relation
The general form of for such processes — on the assumption that the variances are finite — was found by A.N. Kolmogorov [Ko] (a special case of the canonical representation of infinitely-divisible distributions presented below).
The characteristic function of an infinitely-divisible distribution never vanishes, and its logarithm (in the sense of the principal value) permits a representation of the form:
(the so-called Lévy–Khinchin canonical representation), where
is some real constant and is a non-decreasing function of bounded variation with . The integrand is taken to be equal to for . Whatever the value of the constant and of the function with the above properties, formula (*) defines the logarithm of the characteristic function of some infinitely-divisible distribution. The correspondence between infinitely-divisible distributions and pairs is one-to-one and is also bicontinuous. This means that an infinitely-divisible distribution is weakly convergent towards an infinitely-divisible limit distribution if and only if and converges to as .
Examples. Let , , , . Then, in order to have a normal distribution with mathematical expectation and variance in formula (*), one must put
For a Poisson distribution with parameter one has
For a Cauchy distribution with density
one has ,
The canonical representation (*) is convenient from a purely "technical" point of view (owing to the fact that has bounded variation), but the function has no direct probabilistic interpretation. For this reason another form of representation of infinitely-divisible distributions, which permits a direct probabilistic interpretation, is employed as well. Let the functions and be defined, for and respectively, by the formulas:
These functions are non-decreasing, for , and for ; in a neighbourhood of zero the functions may be unbounded. If one denotes by the jump of at zero, formula (*) may be rewritten as follows:
(Lévy's canonical representation). The functions and describe, roughly speaking, the frequency of the jumps of varying quantities in the homogeneous process with independent increments for which
The importance of the role played in the limit theorems of probability theory by infinitely-divisible distributions is due to the fact that these and only these distributions can be the limit distributions for sums of independent random variables subject to the requirement of asymptotic negligibility. Consider the triangular array , of mutually-independent random variables and select mutually-independent random variables with infinitely-divisible distributions (the so-called accompanying infinitely-divisible distributions); the characteristic function of the variable is defined in terms of the characteristic function of the variable so as to preserve the following basic property: The distributions of the sums
converge to the same limit distribution (for a certain selection of the constants ) if and only if the sums
converge to a limit distribution. For a symmetric distribution it is assumed that
In other cases the expression for is more complex, and contains the so-called truncated mathematical expectations of . The properties of infinitely-divisible distributions are described in terms of functions forming part of the canonical representations. For instance, an infinitely-divisible distribution function is continuous if and only if .
An important special case of infinitely-divisible distributions are the so-called stable distributions (cf. Stable distribution). See also Infinitely-divisible distributions, factorization of.
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Infinitely-divisible distribution. Encyclopedia of Mathematics. URL: http://www.encyclopediaofmath.org/index.php?title=Infinitely-divisible_distribution&oldid=26539