关于随机周期性强依赖时间序列，以及在神经呼吸驱动数据中的应用

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Communications in Statistics - Theory and Methods Pub Date : 2024-05-27 DOI:10.1080/03610926.2024.2355582

Jan Beran, Jeremy Näscher, Stephan Walterspacher

引用次数: 0

摘要

我们考虑的时间序列具有长度和形状随机变化的季节性成分。季节过程的形状参数以及噪声成分都是静态的，并表现出...

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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On randomly periodic strongly dependent time series, with applications to neural respiratory drive data

We consider time series with a seasonal component that varies randomly in length and shape. The shape parameters of the seasonal process, as well as the noise component, are stationary and exhibit ...

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来源期刊

Communications in Statistics - Theory and Methods 数学-统计学与概率论

CiteScore

2.00

自引率

12.50%

发文量

320

审稿时长

7.5 months

期刊介绍： The Theory and Methods series intends to publish papers that make theoretical and methodological advances in Probability and Statistics. New applications of statistical and probabilistic methods will also be considered for publication. In addition, special issues dedicated to a specific topic of current interest will also be published in this series periodically, providing an exhaustive and up-to-date review of that topic to the readership.