2020
DOI: 10.1007/s00440-020-00963-0
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On nested infinite occupancy scheme in random environment

Abstract: We consider an infinite balls-in-boxes occupancy scheme with boxes organised in nested hierarchy, and random probabilities of boxes defined in terms of iterated fragmentation of a unit mass. We obtain a multivariate functional limit theorem for the cumulative occupancy counts as the number of balls approaches infinity. In the case of fragmentation driven by a homogeneous residual allocation model our result generalises the functional central limit theorem for the block counts in Ewens' and more general regener… Show more

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Cited by 14 publications
(30 citation statements)
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“…, K (u) n ( ) u≥0 for each ∈ N, properly normalized and centered, as n → ∞. In particular, according to Corollary 4.3 in [15] the following multivariate central limit theorem holds in the GEM(0, 1) case: for each ∈ N, as n → ∞,…”
Section: Resultsmentioning
confidence: 94%
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“…, K (u) n ( ) u≥0 for each ∈ N, properly normalized and centered, as n → ∞. In particular, according to Corollary 4.3 in [15] the following multivariate central limit theorem holds in the GEM(0, 1) case: for each ∈ N, as n → ∞,…”
Section: Resultsmentioning
confidence: 94%
“…This means that (1.1) holds with independent W k 's having a uniform distribution on [0, 1], that is, P{W k ∈ dx} = 1 (0,1) (x)dx. In [15], for a rather wide class of exponentially decaying random probabilities (environments), a functional limit theorem was proved for the random process K (u) n (1), . .…”
Section: Resultsmentioning
confidence: 99%
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“…With the help of a branching property, we obtain the basic decomposition where for are independent copies of which are also independent of the T ( v ), . Motivated by an application to certain nested infinite occupancy schemes in a random environment, the authors of the recent article [4] proved functional limit theorems in for with appropriate centering and normalizing functions and . The standing assumption of [4] is that the positions are given by , where , are positive random variables with an arbitrary joint distribution satisfying a.s.…”
Section: Introductionmentioning
confidence: 99%