A Smooth Block Bootstrap for Statistical Functionals and Time Series
A Smooth Block Bootstrap for Statistical Functionals and Time Series
复制标题
统计泛函和时间序列的平滑块引导
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
D. Nordman
中科院分区：
文献类型：
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作者：
Karl B. Gregory;S. Lahiri;D. Nordman
与独立数据不同，平滑引导程序很少考虑时间序列，尽管重采样中的数据平滑可以改进引导程序近似，特别是当目标分布取决于平滑总体数量（例如边际密度）时。为了近似通过统计函数（例如 LL 估计器和样本分位数）制定的广泛类别统计数据，我们通过修改最先进的（扩展）锥形块引导程序（TBB）提出了平滑引导程序。我们的处理表明，平滑 TBB 适用于未与其他 TBB 版本正式建立的时间序列推理案例。模拟还表明平滑增强了块引导。
Unlike with independent data, smoothed bootstraps have received little consideration for time series, although data smoothing within resampling can improve bootstrap approximations, especially when target distributions depend on smooth population quantities (e.g., marginal densities). For approximating a broad class statistics formulated through statistical functionals (e.g., LL‐estimators, and sample quantiles), we propose a smooth bootstrap by modifying a state‐of‐the‐art (extended) tapered block bootstrap (TBB). Our treatment shows that the smooth TBB applies to time series inference cases not formally established with other TBB versions. Simulations also indicate that smoothing enhances the block bootstrap.