An exponential nonuniform Berry–Esseen bound of the maximum likelihood estimator in a Jacobi process

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Applied Probability Pub Date : 2024-02-14 DOI:10.1017/jpr.2023.100

Hui Jiang, Qihao Lin, Shaochen Wang

引用次数: 0

Abstract

We establish the exponential nonuniform Berry–Esseen bound for the maximum likelihood estimator of unknown drift parameter in an ultraspherical Jacobi process using the change of measure method and precise asymptotic analysis techniques. As applications, the optimal uniform Berry–Esseen bound and optimal Cramér-type moderate deviation for the corresponding maximum likelihood estimator are obtained.

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雅可比过程中最大似然估计子的指数非均匀贝里-埃森边界

我们利用度量变化方法和精确渐近分析技术，为超球面雅可比过程中未知漂移参数的最大似然估计值建立了指数非均匀贝里-埃森约束。作为应用，得到了相应最大似然估计器的最优均匀贝里-埃森约束和最优克拉梅尔型中等偏差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Applied Probability 数学-统计学与概率论

CiteScore

1.50

自引率

10.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Applied Probability is the oldest journal devoted to the publication of research in the field of applied probability. It is an international journal published by the Applied Probability Trust, and it serves as a companion publication to the Advances in Applied Probability. Its wide audience includes leading researchers across the entire spectrum of applied probability, including biosciences applications, operations research, telecommunications, computer science, engineering, epidemiology, financial mathematics, the physical and social sciences, and any field where stochastic modeling is used. A submission to Applied Probability represents a submission that may, at the Editor-in-Chief’s discretion, appear in either the Journal of Applied Probability or the Advances in Applied Probability. Typically, shorter papers appear in the Journal, with longer contributions appearing in the Advances.