On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators
On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators
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关于非参数核时间序列回归、自回归和密度估计量的一致性和有限样本特性
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发表时间：
1986
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影响因子：
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通讯作者：
P. Robinson
中科院分区：
文献类型：
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作者：
P. Robinson
摘要在向量值平稳时间序列的背景下，研究了条件期望和联合概率密度的核估计。在极小矩条件下，弱相关和带宽条件下，建立了弱相容性。在这些条件下，一些有限样本理论探讨了序列依赖性对估计量变异性的影响，以及它对带宽选择的影响。
SummaryKernel estimators of conditional expectations and joint probability densities are studied in the context of a vector-valued stationary time series. Weak consistency is established under minimal moment conditions and under a hierarchy of weak dependence and bandwidth conditions. Prompted by these conditions, some finite-sample theory explores the effect of serial dependence on variability of estimators, and its implications for choice of bandwidth.