2010
DOI: 10.15388/lmr.2014.12
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Theorems on large deviations for the sum of random number of summands

Abstract: In this paper, we present the rate of convergence of normal approximation and the theorem on large deviations for a compound process Zt = \sumNt i=1 t aiXi, where Z0 = 0 and ai > 0, of weighted independent identically distributed random variables Xi, i = 1, 2, . . . with  mean EXi = µ and variance DXi = σ2 > 0. It is assumed that Nt is a non-negative integervalued random variable, which depends on t > 0 and is independent of Xi, i = 1, 2, . . . .

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“…(6) in two cases: µ = 0 and µ = 0 by pointing out the difference between them. It should be noted that large deviations when µ = 0 have been already considered in our papers [1,2], thus in this instance we pointed only some results without proofs.…”
Section: Introductionmentioning
confidence: 79%
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“…(6) in two cases: µ = 0 and µ = 0 by pointing out the difference between them. It should be noted that large deviations when µ = 0 have been already considered in our papers [1,2], thus in this instance we pointed only some results without proofs.…”
Section: Introductionmentioning
confidence: 79%
“…Undoubtedly, there are a large amount of literature on theorems of large deviations for the r.s. under different assumptions and with various applications, for example, [1,2,[18][19][20][21][22][23][24]. Unfortunately, as far as we know, without our papers (see [1,2,21]) there are only few papers, e.g., [18,23,24], accordingly, by Aksomaitis (1965), Statulevičius (1967), Saulis and Deltuvienė (2007) on large deviations for the r.s.…”
Section: Introductionmentioning
confidence: 99%
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