Stable convergence of conditional least squares estimators for supercritical continuous state and continuous time branching processes with immigration

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY Journal of Statistical Planning and Inference Pub Date : 2024-07-22 DOI:10.1016/j.jspi.2024.106213

Mátyás Barczy

引用次数: 0

Abstract

We prove stable convergence of conditional least squares estimators of drift parameters for supercritical continuous state and continuous time branching processes with immigration based on discrete time observations.

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有移民的超临界连续状态和连续时间分支过程的条件最小二乘估计子的稳定收敛性

我们证明了超临界连续状态和连续时间分支过程的漂移参数条件最小二乘法估计值的稳定收敛性，并基于离散时间观测结果进行了移民。

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来源期刊

Journal of Statistical Planning and Inference 数学-统计学与概率论

CiteScore

2.10

自引率

11.10%

发文量

审稿时长

3-6 weeks

期刊介绍： The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.

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