Results 1  10
of
10
Functional Limit Theorems For Multitype Branching Processes And Generalized Pólya Urns
 APPL
, 2004
"... A functional limit theorem is proved for multitype continuous time Markov branching processes. As consequences, we obtain limit theorems for the branching process stopped by some stopping rule, for example when the total number of particles reaches a given level. Using the ..."
Abstract

Cited by 110 (18 self)
 Add to MetaCart
(Show Context)
A functional limit theorem is proved for multitype continuous time Markov branching processes. As consequences, we obtain limit theorems for the branching process stopped by some stopping rule, for example when the total number of particles reaches a given level. Using the
Asymptotic normality of the kcore in random graphs
, 2006
"... We study the kcore of a random (multi)graph on n vertices with a given degree sequence. In our previous paper [Random Structures Algorithms 30 (2007) 50–62] we used properties of empirical distributions of independent random variables to give a simple proof of the fact that the size of the giant k ..."
Abstract

Cited by 20 (8 self)
 Add to MetaCart
We study the kcore of a random (multi)graph on n vertices with a given degree sequence. In our previous paper [Random Structures Algorithms 30 (2007) 50–62] we used properties of empirical distributions of independent random variables to give a simple proof of the fact that the size of the giant kcore obeys a law of large numbers as n →∞. Here we develop the method further and show that the fluctuations around the deterministic limit converge to a Gaussian law above and near the threshold, and to a nonnormal law at the threshold. Further, we determine precisely the location of the phase transition window for the emergence of a giant kcore. Hence, we deduce corresponding results for the kcore in G(n, p) and G(n, m).
Susceptibility in subcritical random graphs
, 2008
"... We study the evolution of the susceptibility in the subcritical random graph G(n, p) as n tends to infinity. We obtain precise asymptotics of its expectation and variance, and show it obeys a law of large numbers. We also prove that the scaled fluctuations of the susceptibility around its determin ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
We study the evolution of the susceptibility in the subcritical random graph G(n, p) as n tends to infinity. We obtain precise asymptotics of its expectation and variance, and show it obeys a law of large numbers. We also prove that the scaled fluctuations of the susceptibility around its deterministic limit converge to a Gaussian law. We further extend our results to higher moments of the component size of a random vertex, and prove that they are jointly asymptotically normal.
Sorting using complete subintervals and the maximum number of runs in a randomly evolving sequence
 Ann. Comb
"... Abstract. We study the space requirements of a sorting algorithm where only items that at the end will be adjacent are kept together. This is equivalent to the following combinatorial problem: Consider a string of fixed length n that starts as a string of 0’s, and then evolves by changing each 0 to ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
(Show Context)
Abstract. We study the space requirements of a sorting algorithm where only items that at the end will be adjacent are kept together. This is equivalent to the following combinatorial problem: Consider a string of fixed length n that starts as a string of 0’s, and then evolves by changing each 0 to 1, with the n changes done in random order. What is the maximal number of runs of 1’s? We give asymptotic results for the distribution and mean. It turns out that, as in many problems involving a maximum, the maximum is asymptotically normal, with fluctuations of order n 1/2, and to the first order well approximated by the number of runs at the instance when the expectation is maximized, in this case when half the elements have changed to 1; there is also a second order term of order n 1/3. We also treat some variations, including priority queues. The proofs use methods originally developed for random graphs. 1.
Rademacher Chaos: Tail Estimates vs Limit Theorems
"... We study Rademacher chaos indexed by a sparse set which has a fractional combinatorial dimension. We obtain tail estimates for nite sums and a normal limit theorem as the size tends to in nity. The tails for nite sums may be much larger that the tails of the limit. ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We study Rademacher chaos indexed by a sparse set which has a fractional combinatorial dimension. We obtain tail estimates for nite sums and a normal limit theorem as the size tends to in nity. The tails for nite sums may be much larger that the tails of the limit.
Monotonicity, asymptotic normality and vertex degrees in random graphs
 Bernoulli
, 2007
"... Abstract. We exploit a result by Nerman [23] which shows that conditional limit theorems hold when a certain monotonicity condition is satisfied. Our main result is an application to vertex degrees in random graphs where we obtain asymptotic normality for the number of vertices with a given degree i ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract. We exploit a result by Nerman [23] which shows that conditional limit theorems hold when a certain monotonicity condition is satisfied. Our main result is an application to vertex degrees in random graphs where we obtain asymptotic normality for the number of vertices with a given degree in the random graph G(n, m) with a fixed number of edges from the corresponding result for the random graph G(n, p) with independent edges. We give also some simple applications to random allocations and to spacings. Finally, inspired by these results but logically independent from them, we investigate whether a onesided version of the Cramér–Wold theorem holds. We show that such a version holds under a weak supplementary condition, but not without it. 1.