Efficiency improvements in inference on stationary and nonstationary fractional time series
平稳与非平稳分数阶时间序列推断的效率改进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。
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：摘要直接研究分数阶积分、平稳与非平稳过程及 ARMA 模型的推断。
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…
关注这篇论文的作者
平稳与非平稳分数阶时间序列推断的效率改进
- 研究问题
- 在分数阶积分阶数未知、存在确定性趋势且创新分布未知时，如何对模型参数进行有效的渐近推断？
- 方法与思路
- 作者构造使用非参数级数估计的半参数方法，处理自回归表示截断形成创新代理的误差，并开展有限样本模拟。
- 材料陈述的贡献
- 摘要称，参数估计渐近正态并达到所讨论的 M 估计效率界；相关分析也为非分数阶时间序列模型提供方法结果。
- 阅读关注点
- 应核对积分阶数与趋势的允许范围、级数估计条件、截断误差控制，以及效率界的参照模型和模拟设定。
- 阅读边界
- 仅据版本未注明的索引摘要；摘要未给出完整条件及模拟数值，达到特定效率界不能解释为对所有方法均更优。
原文摘要
We consider a time series model involving a fractional stochastic component, whose integration order can lie in the stationary/invertible or nonstationary regions and be unknown, and an additive deterministic component consisting of a generalized polynomial. The model can thus incorporate competing descriptions of trending behavior. The stationary input to the stochastic component has parametric autocorrelation, but innovation with distribution of unknown form. The model is thus semiparametric, and we develop estimates of the parametric component which are asymptotically normal and achieve an M-estimation efficiency bound, equal to that found in work using an adaptive LAM/LAN approach. A major technical feature which we treat is the effect of truncating the autoregressive representation in order to form innovation proxies. This is relevant also when the innovation density is parameterized, and we provide a result for that case also. Our semiparametric estimates employ nonparametric series estimation, which avoids some complications and conditions in kernel approaches featured in much work on adaptive estimation of time series models; our work thus also contributes to methods and theory for nonfractional time series models, such as autoregressive moving averages. A Monte Carlo study of finite sample performance of the semiparametric estimates is included.