Self-Normalization for Time Series: A Review of Recent Developments
时间序列自归一化：近期进展综述AI 解读 ↓
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时间序列自归一化：近期进展综述
- 研究问题
- 自归一化如何用于不同依赖结构下的时间序列推断与变化点检测，各类应用之间有哪些联系、优势和限制？
- 方法与思路
- 作者综述平稳、长记忆、局部平稳及近单位根等情形，并讨论回归、函数型序列及固定带宽渐近框架下的相关方法。
- 材料陈述的贡献
- 摘要称文章梳理多类自归一化推断的联系，介绍若干新变体和额外模拟，并总结时间序列应用中的局限与研究方向。
- 阅读关注点
- 应按数据依赖结构核对自归一化统计量、极限分布与适用条件，并比较变化点检测和参数区间推断的不同要求。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；这是综述，摘要未给出各方法的完整条件或新变体细节，不能据此替代原始证明或推断刊本变化。
原文摘要
This article reviews some recent developments on the inference of time series data using the self-normalized approach. We aim to provide a detailed discussion about the use of self-normalization in different contexts and highlight distinctive feature associated with each problem and connections among these recent developments. The topics covered include: confidence interval construction for a parameter in a weakly dependent stationary time series setting, change point detection in the mean, robust inference in regression models with weakly dependent errors, inference for nonparametric time series regression, inference for long memory time series, locally stationary time series and near-integrated time series, change point detection, and two-sample inference for functional time series, as well as the use of self-normalization for spatial data and spatial-temporal data. Some new variations of the self-normalized approach are also introduced with additional simulation results. We also provide a brief review of related inferential methods, such as blockwise empirical likelihood and subsampling, which were recently developed under the fixed-b asymptotic framework. We conclude the article with a summary of merits and limitations of self-normalization in the time series context and potential topics for future investigation.