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Self-normalized Cramér moderate deviations for a supercritical Galton–Watson process

Part of: Markov processes Limit theorems Parametric inference

Published online by Cambridge University Press:  24 April 2023

Xiequan Fan  and
Qi-Man Shao
Xiequan Fan*
Affiliation:
Northeastern University at Qinhuangdao
Qi-Man Shao*
Affiliation:
Southern University of Science and Technology
*
*Postal address: School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, Hebei, China. Email address: [email protected]
**Postal address: Department of Statistics and Data Science, SICM, National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China. Email address: [email protected]
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Abstract

Let $(Z_n)_{n\geq0}$ be a supercritical Galton–Watson process. Consider the Lotka–Nagaev estimator for the offspring mean. In this paper we establish self-normalized Cramér-type moderate deviations and Berry–Esseen bounds for the Lotka–Nagaev estimator. The results are believed to be optimal or near-optimal.

Keywords

Self-normalized processesLotka–Nagaev estimatorBerry–Esseen boundsoffspring mean

MSC classification

Primary: 60F10: Large deviations 60J80: Branching processes (Galton-Watson, birth-and-death, etc.)
Secondary: 62F12: Asymptotic properties of estimators 62F03: Hypothesis testing
Type
Original Article
Information
Journal of Applied Probability , Volume 60 , Issue 4 , December 2023 , pp. 1281 - 1292
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Applied Probability Trust

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