0000000000485047

AUTHOR

H. J. Schuh

showing 2 related works from this author

Random Logistic Maps II. The Critical Case

2003

Let (X n )∞ 0 be a Markov chain with state space S=[0,1] generated by the iteration of i.i.d. random logistic maps, i.e., X n+1=C n+1 X n (1−X n ),n≥0, where (C n )∞ 1 are i.i.d. random variables with values in [0, 4] and independent of X 0. In the critical case, i.e., when E(log C 1)=0, Athreya and Dai(2) have shown that X n → P 0. In this paper it is shown that if P(C 1=1)<1 and E(log C 1)=0 then (i) X n does not go to zero with probability one (w.p.1) and in fact, there exists a 0<β<1 and a countable set ▵⊂(0,1) such that for all x∈A≔(0,1)∖▵, P x (X n ≥β for infinitely many n≥1)=1, where P x stands for the probability distribution of (X n )∞ 0 with X 0=x w.p.1. A is a closed set for (X n…

Statistics and ProbabilityCombinatoricsDiscrete mathematicsDistribution (mathematics)Multivariate random variableInitial distributionGeneral MathematicsZero (complex analysis)Random elementProbability distributionStatistics Probability and UncertaintyRandom variableMathematicsJournal of Theoretical Probability
researchProduct

A Galton–Watson process with a threshold

2016

Abstract In this paper we study a special class of size dependent branching processes. We assume that for some positive integer K as long as the population size does not exceed level K, the process evolves as a discrete-time supercritical branching process, and when the population size exceeds level K, it evolves as a subcritical or critical branching process. It is shown that this process does die out in finite time T. The question of when the mean value E(T) is finite or infinite is also addressed.

Statistics and ProbabilityGeneral MathematicsPopulation size010102 general mathematicsMean valueProcess (computing)01 natural sciencesGalton–Watson processBranching (linguistics)010104 statistics & probabilityIntegerStatistical physics0101 mathematicsStatistics Probability and UncertaintyFinite timeMathematicsBranching processJournal of Applied Probability
researchProduct