Asymptotic Inference for Partially Observed Branching Processes

J 2011

Asymptotic Inference for Partially Observed Branching Processes

KVITKOVIČOVÁ, Andrea a VM PANARETOS

Základní údaje

Originální název

Asymptotic Inference for Partially Observed Branching Processes

Autoři

KVITKOVIČOVÁ, Andrea a VM PANARETOS

Vydání

Advances in Applied Probability, Sheffield, Applied Probability Trust, 2011, 0001-8678

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 0.679

UT WoS

000298713900012

Klíčová slova anglicky

Epidemic model; Galton-Watson branching process; partial observation; consistency; asymptotic distribution; martingale; stable convergence

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 1. 2016 00:19, Mgr. Andrea Kraus, M.Sc., Ph.D.

Anotace

V originále

We consider the problem of estimation in a partially observed discrete-time Galton-Watson branching process, focusing on the first two moments of the offspring distribution. Our study is motivated by modelling the counts of new cases at the onset of a stochastic epidemic, allowing for the facts that only a part of the cases is detected, and that the detection mechanism may affect the evolution of the epidemic. In this setting, the offspring mean is closely related to the spreading potential of the disease, while the second moment is connected to the variability of the mean estimators. Inference for branching processes is known for its nonstandard characteristics, as compared with classical inference. When, in addition, the true process cannot be directly observed, the problem of inference suffers significant further perturbations. We propose nonparametric estimators related to those used when the underlying process is fully observed, but suitably modified to take into account the intricate dependence structure induced by the partial observation and the interaction scheme. We show consistency, derive the limiting laws of the estimators, and construct asymptotic confidence intervals, all valid conditionally on the explosion set.

Citovat

KVITKOVIČOVÁ, Andrea a VM PANARETOS. Asymptotic Inference for Partially Observed Branching Processes. Advances in Applied Probability. Sheffield: Applied Probability Trust, 2011, roč. 43, č. 4, s. 1166-1190. ISSN 0001-8678.

@article{1324047,
   author = {Kvitkovičová, Andrea and Panaretos, VM},
   article_location = {Sheffield},
   article_number = {4},
   keywords = {Epidemic model; Galton-Watson branching process; partial observation; consistency; asymptotic distribution; martingale; stable convergence},
   language = {eng},
   issn = {0001-8678},
   journal = {Advances in Applied Probability},
   title = {Asymptotic Inference for Partially Observed Branching Processes},
   volume = {43},
   year = {2011}
}

TY  - JOUR
ID  - 1324047
AU  - Kvitkovičová, Andrea - Panaretos, VM
PY  - 2011
TI  - Asymptotic Inference for Partially Observed Branching Processes
JF  - Advances in Applied Probability
VL  - 43
IS  - 4
SP  - 1166-1190
EP  - 1166-1190
PB  - Applied Probability Trust
SN  - 00018678
KW  - Epidemic model
KW  - Galton-Watson branching process
KW  - partial observation
KW  - consistency
KW  - asymptotic distribution
KW  - martingale
KW  - stable convergence
N2  - We consider the problem of estimation in a partially observed discrete-time Galton-Watson branching process, focusing on the first two moments of the offspring distribution. Our study is motivated by modelling the counts of new cases at the onset of a stochastic epidemic, allowing for the facts that only a part of the cases is detected, and that the detection mechanism may affect the evolution of the epidemic. In this setting, the offspring mean is closely related to the spreading potential of the disease, while the second moment is connected to the variability of the mean estimators. Inference for branching processes is known for its nonstandard characteristics, as compared with classical inference. When, in addition, the true process cannot be directly observed, the problem of inference suffers significant further perturbations. We propose nonparametric estimators related to those used when the underlying process is fully observed, but suitably modified to take into account the intricate dependence structure induced by the partial observation and the interaction scheme. We show consistency, derive the limiting laws of the estimators, and construct asymptotic confidence intervals, all valid conditionally on the explosion set.
ER  -

KVITKOVIČOVÁ, Andrea a VM PANARETOS. Asymptotic Inference for Partially Observed Branching Processes. \textit{Advances in Applied Probability}. Sheffield: Applied Probability Trust, 2011, roč.~43, č.~4, s.~1166-1190. ISSN~0001-8678.

Přehled o publikaci