The survival probability of a critical branching process in a Markovian random environment

Emile Le Page; Yinna Ye

doi:10.1016/j.crma.2010.01.014

The survival probability of a critical branching process in a Markovian random environment

Emile Le Page^*
, Yinna Ye

^*Corresponding author for this work

Université de Bretagne Sud
Université de Tours

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

In this Note, we first prove a local limit theorem for a semi-Markov chain and then apply it to study the asymptotic behavior of the survival probability of a critical branching process in Markovian random environment.

Original language	English
Pages (from-to)	301-304
Number of pages	4
Journal	Comptes Rendus Mathematique
Volume	348
Issue number	5-6
DOIs	https://doi.org/10.1016/j.crma.2010.01.014
Publication status	Published - Mar 2010
Externally published	Yes

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title = "The survival probability of a critical branching process in a Markovian random environment",

abstract = "In this Note, we first prove a local limit theorem for a semi-Markov chain and then apply it to study the asymptotic behavior of the survival probability of a critical branching process in Markovian random environment.",

author = "\{Le Page\}, Emile and Yinna Ye",

year = "2010",

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doi = "10.1016/j.crma.2010.01.014",

language = "English",

volume = "348",

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journal = "Comptes Rendus Mathematique",

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AU - Le Page, Emile

AU - Ye, Yinna

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AB - In this Note, we first prove a local limit theorem for a semi-Markov chain and then apply it to study the asymptotic behavior of the survival probability of a critical branching process in Markovian random environment.

UR - https://www.scopus.com/pages/publications/77649235713

U2 - 10.1016/j.crma.2010.01.014

DO - 10.1016/j.crma.2010.01.014

M3 - Article

AN - SCOPUS:77649235713

SN - 1631-073X

VL - 348

SP - 301

EP - 304

JO - Comptes Rendus Mathematique

JF - Comptes Rendus Mathematique

IS - 5-6

ER -

The survival probability of a critical branching process in a Markovian random environment

Abstract

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