Results 221 to 230 of about 475,253 (253)

Infinitely Divisible Laws

1978
Row sums \( \mathop \sum \nolimits_{i = 1}^{{k_n}} {X_{ni}} \) S of arrays of random variables {Xni1 ≤ i≤kn >→ ∞n ≥ 1} that are rowwise independent have been considered briefly with respect to the Marcinkiewicz–Zygmund type strong laws of large numbers (Example 10.4.1).
Yuan Shih Chow, Henry Teicher
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Infinite divisibility II

Journal of Theoretical Probability, 1988
[For part I see Adv. Math. 69, No.1, 115-132 (1988; Zbl 0646.60017).] Let G be a commutative topological group which is separated by its continuous characters. Consider a triangular array of distributions on G with the property that its row-wise convolution products converge to a limit \(\mu\).
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Infinitely Divisible Distributions

1975
A distribution function F (x) and the corresponding c.f. f (t) are said to be infinitely divisible if for every positive integer n there exists a c.f. f n (t) such that $$f\left( t \right) = {\left( {{f_n}\left( t \right)} \right)^n}$$ (1.1)
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Infinitely Divisible Processes

2014
In Chapter 11 we investigate infinitely divisible processes in a far more general setting than what mainstream probability theory has yet considered: we make no assumption of stationarity of increments of any kind and our processes are actually indexed by an abstract set.
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Infinitely Divisible Processes

2016
Infinitely divisible stochastic processes form a broad family whose structure is reasonably well understood. A stochastic process \({\bigl (X(t),\,t \in T\bigr )}\) is said to be infinitely divisible if for every n = 1, 2, …, there is a stochastic process \(\bigl( Y(t),\, t\in T\bigr)\) such ...
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Infinitely divisible distributions in turbulence

Physical Review E, 1994
The imbedding of the scale similarity of random fields into the theory of infinitely divisible probability distributions is considered. The general probability distribution for the breakdown coefficients of turbulent energy dissipation is obtained along with corresponding similarity exponents.
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Infinitely Divisible Distributions

2014
For every n, the normal distribution with expectation μ and variance σ 2 is the nth convolution power of a probability measure (namely of the normal distribution with expectation μ/n and variance σ 2/n). This property is called infinite divisibility and is shared by other probability distributions such as the Poisson distribution and the Gamma ...
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Convolution equivalence and infinite divisibility

Journal of Applied Probability, 2004
Known results relating the tail behaviour of a compound Poisson distribution function to that of its Lévy measure when one of them is convolution equivalent are extended to general infinitely divisible distributions. A tail equivalence result is obtained for random sum distributions in which the summands have a two-sided distribution.
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On the Infinite Divisibility of Polynomials in Infinitely Divisible Random Variables

1992
It is shown that all second degree polynomials in standard normal random variables are infinitely divisible and an example of a polynomial of degree three or more is given which is not infinitely divisible. It is also shown that if a polynomial in a random variable with support {0,1,2,…} is infinitely divisible then it must be linear.
V. K. Rohatgi, G. J. Székely
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Infinitely Divisible Point Processes.

Journal of the Royal Statistical Society. Series A (General), 1979
Alan F. Karr, K. Matthes, J. Kerstan, J. Mecke   +3 more
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