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Adaptive estimation for Weakly Dependent Functional Times Series

Hassan Maissoro · Valentin Patilea · Myriam Vimond首次提交 2024-03-20 · 修订 2024-03-20 · v1arXiv预印本

弱依赖函数型时间序列的自适应估计AI 解读

The local regularity of functional time series is studied under $L^p-m-$ appro\-ximability assumptions. The sample paths are observed with error at possibly random design points. Non-asymptotic concentration bounds of the regularity estimators are derived. As an application, we build nonparametric mean and autocovariance functions estimators that adapt to the regularity and the design, which can be sparse or dense. We also derive the asymptotic normality of the mean…The local regularity of functional time series is studied under $L^p-m-$appro\-ximability assumptions. The sample paths are observed with error at possibly random design points. Non-asymptotic concentration bounds of the regularity estimators are derived. As an application, we build nonparametric mean and autocovariance functions estimators that adapt to the regularity and the design, which can be sparse or dense. We also derive the asymptotic normality of the mean…

宏观 · 时间序列半参数与非参数

关联记录被引 0 次 ↗OpenAlex · 核验 2026-09-30

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