长记忆噪声驱动下随机采样模型最小二乘估计的一致性:更新情况

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica Pub Date : 2023-01-01 DOI:10.5705/ss.202020.0457

Héctor Araya, Natalia Bahamonde, Lisandro Fermín, Tania Roa, Soledad Torres

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引用次数: 1

摘要

本文证明了具有长记忆噪声的随机抽样线性回归模型的最小二乘估计量的强相合性。此外，我们还说明了如何处理时间T = 1之前的随机观测数。仿真研究说明了不同项的行为，以及估计器在不同Hurst参数H值下的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

ON THE CONSISTENCY OF THE LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE RENEWAL CASE

In this study, we prove the strong consistency of the least squares estimator in a random sampled linear regression model with long-memory noise and an independent set of random times given by renewal process sampling. Additionally, we illustrate how to work with a random number of observations up to time T = 1. A simulation study is provided to illustrate the behavior of the different terms, as well as the performance of the estimator under various values of the Hurst parameter H.

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来源期刊

Statistica Sinica 数学-统计学与概率论

CiteScore

2.10

自引率

0.00%

发文量

审稿时长

10.5 months

期刊介绍： Statistica Sinica aims to meet the needs of statisticians in a rapidly changing world. It provides a forum for the publication of innovative work of high quality in all areas of statistics, including theory, methodology and applications. The journal encourages the development and principled use of statistical methodology that is relevant for society, science and technology.