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Journal articles

Late levels of nested occupancy scheme in random environment

Abstract : Consider a weighted branching process generated by a point process on $[0,1]$, whose atoms sum up to one. Then the weights of all individuals in any given generation sum up to one, as well. We define a nested occupancy scheme in random environment as the sequence of balls-in-boxes schemes (with random probabilities) in which boxes of the $j$th level, $j=1,2,\ldots$ are identified with the $j$th generation individuals and the hitting probabilities of boxes are identified with the corresponding weights. The collection of balls is the same for all generations, and each ball starts at the root and moves along the tree of the weighted branching process according to the following rule: transition from a mother box to a daughter box occurs with probability given by the ratio of the daughter and mother weights. Assuming that there are $n$ balls, we give a full classification of regimes of the a.s.\ convergence for the number of occupied (ever hit) boxes in the $j$th level, properly normalized, as $n$ and $j=j_n$ grow to $\infty$. Here, $(j_n)_{n\in\mathbb N}$ is a sequence of positive numbers growing proportionally to $\log n$. We call such levels late, for the nested occupancy scheme gets extinct in the levels which grow super-logarithmically in $n$ in the sense that each occupied box contains one ball. Also, in some regimes we prove the strong laws of large numbers (a) for the number of the $j$th level boxes which contain at least $k$ balls, $k\geq 2$ and (b) under the assumption that the mean number of the first level boxes is finite, for the number of empty boxes in the $j$th level.
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Contributor : Bastien Mallein Connect in order to contact the contributor
Submitted on : Wednesday, January 26, 2022 - 10:28:34 PM
Last modification on : Thursday, March 17, 2022 - 10:08:18 AM
Long-term archiving on: : Wednesday, April 27, 2022 - 7:35:56 PM


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Alexander Iksanov, Bastien Mallein. Late levels of nested occupancy scheme in random environment. Stochastic Models, INFORMS (Institute for Operations Research and Management Sciences), 2022, ⟨10.1080/15326349.2021.1984948⟩. ⟨hal-03354403⟩



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