Given a real-valued continuous function ƒ on the half-line [0,∞) we denote by P*(ƒ) the set of all probability measures μ on [0,∞) with finite ƒ-moments $ʃ_{0}^{∞} ƒ(x)μ^{*n}(dx)$ (n = 1,2...). A function ƒ is said to have the identification property} if probability measures from P*(ƒ) are uniquely determined by their ƒ-moments. A function ƒ is said to be a Bernstein function} if it is infinitely differentiable on the open half-line (0,∞) and $(-1)^{n} ƒ^{(n+1)}(x)$ is completely monotone for some nonnegative integer n. The purpose of this paper is to give a necessary and sufficient condition in terms of the representing measures for Bernstein functions to have the identification property.
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The paper deals with nonnegative stochastic processes X(t,ω)(t ≤ 0) not identically zero with stationary and independent increments right-continuous sample functions and fulfilling the initial condition X(0,ω)=0. The main aim is to study the moments of the random functionals $\int_0^∞ f(X(τ,ω))dτ$ for a wide class of functions f. In particular a characterization of deterministic processes in terms of the exponential moments of these functionals is established.
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The paper is devoted to the study of integral functionals $ʃ_0^∞ f(X(t,ω))dt$ for a wide class of functions f and transient stochastic processes X(t,ω) with stationary and independent increments. In particular, for nonnegative processes a random analogue of the Tauberian theorem is obtained.
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The paper is devoted to the study of stationary random sequences. A concept of dual sequences is discussed. The main aim of the paper is to establish a relationship between the errors of linear least squares predictions for sequences and their duals.
The paper is devoted to the study of integral functionals $ʃ_0^∞ f(X(t,ω)) dt$ for continuous nonincreasing functions f and nonnegative stochastic processes X(t,ω) with stationary and independent increments. In particular, a concept of stability defined in terms of the functionals $ʃ_0^∞ f(aX(t,ω))dt$ with a ∈ (0,∞) is discussed.
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By a harmonizable sequence of random variables we mean the sequence of Fourier coefficients of a random measure M: $Xₙ(M) = ∫_{0}^{1} e^{2πnis}M(ds)$ (n = 0,±1,...) The paper deals with prediction problems for sequences {Xₙ(M)} for isotropic and atomless random measures M. The crucial result asserts that the space of all complex-valued M-integrable functions on the unit interval is a Musielak-Orlicz space. Hence it follows that the problem for {Xₙ(M)} (n = 0,±1,...) to be deterministic is in fact an extremal problem of Szegö's type for Musielak-Orlicz spaces in question. This leads to a characterization of deterministic sequences {Xₙ(M)} (n = 0,±1,...) in terms of random measures M.
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For a random vector X with a fixed distribution μ we construct a class of distributions ℳ(μ) = {μ∘λ: λ ∈ 𝓟}, which is the class of all distributions of random vectors XΘ, where Θ is independent of X and has distribution λ. The problem is to characterize the distributions μ for which ℳ(μ) is closed under convolution. This is equivalent to the characterization of the random vectors X such that for all random variables Θ₁, Θ₂ independent of X, X' there exists a random variable Θ independent of X such that $XΘ₁ + X'Θ₂ \stackrel{d}{=} XΘ$. We show that for every X this property is equivalent to the following condition: ∀ a,b ∈ ℝ ∃ Θ independent of X, $aX + bX' \stackrel{d}{=} XΘ$. This condition reminds the characterizing condition for symmetric stable random vectors, except that Θ is here a random variable, instead of a constant. The above problem has a direct connection with the concept of generalized convolutions and with the characterization of the extreme points for the set of pseudo-isotropic distributions.
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