Novel Models for Multiple Dependent Heteroskedastic Time Series
多条相依异方差时间序列的新模型AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要核心是多条相依时间序列的AR与GARCH建模；数据来自fMRI，未提供金融波动率应用证据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
多条相依异方差时间序列的新模型
- 研究问题
- 对于婴儿fMRI中因突然头部运动而出现高波动的多条相依序列，如何刻画跨脑区共同波动，并评估不同运动程度下的拟合？
- 方法与思路
- 作者考察AR与GARCH组合建模，针对多条相依fMRI序列估计动态与条件方差，并利用统计检验和多项指标评价表现。
- 材料陈述的贡献
- 摘要称，在头部运动较多的数据中，AR与GARCH组合可捕捉跨脑区共享的波动聚集，而运动较少的数据呈现不同拟合表现。
- 阅读关注点
- 应核对多序列依赖如何进入模型、参数估计与传统方法有何差异，以及评价指标是否区分样本内拟合和样本外预测。
- 阅读边界
- 这是arXiv预印本，同行评审或接收状态未核实；摘要未给出具体模型形式及结果数值，金融应用仅属展望，不能据此认定已验证。
原文摘要
Functional magnetic resonance imaging or functional MRI (fMRI) is a very popular tool used for differing brain regions by measuring brain activity. It is affected by physiological noise, such as head and brain movement in the scanner from breathing, heart beats, or the subject fidgeting. The purpose of this paper is to propose a novel approach to handling fMRI data for infants with high volatility caused by sudden head movements. Another purpose is to evaluate the volatility modelling performance of multiple dependent fMRI time series data. The models examined in this paper are AR and GARCH and the modelling performance is evaluated by several statistical performance measures. The conclusions of this paper are that multiple dependent fMRI series data can be fitted with AR + GARCH model if the multiple fMRI data have many sudden head movements. The GARCH model can capture the shared volatility clustering caused by head movements across brain regions. However, the multiple fMRI data without many head movements have fitted AR + GARCH model with different performance. The conclusions are supported by statistical tests and measures. This paper highlights the difference between the proposed approach from traditional approaches when estimating model parameters and modelling conditional variances on multiple dependent time series. In the future, the proposed approach can be applied to other research fields, such as financial economics, and signal processing. Code is available at \url{https://github.com/13204942/STAT40710}.