论文标题
高维面板功能时间序列的统计推断
Statistical Inference for High Dimensional Panel Functional Time Series
论文作者
论文摘要
在本文中，我们为高维功能时间序列开发了统计推断工具。我们在方形集成函数的空间中介绍了一个新的依赖性过程概念，该过程采用了这些空间中功能数据的基础分解的概念，并得出高维功能时间序列的总和的高斯和乘数自举近似值。这些结果具有许多重要的统计后果。示例性地，我们考虑了平均功能的关节同时置信带的发展，并为假设的假设构建了空间维度中的平均功能是平行的。通过一项小型模拟研究和加拿大温度数据的分析来说明结果。
In this paper we develop statistical inference tools for high dimensional functional time series. We introduce a new concept of physical dependent processes in the space of square integrable functions, which adopts the idea of basis decomposition of functional data in these spaces, and derive Gaussian and multiplier bootstrap approximations for sums of high dimensional functional time series. These results have numerous important statistical consequences. Exemplarily, we consider the development of joint simultaneous confidence bands for the mean functions and the construction of tests for the hypotheses that the mean functions in the spatial dimension are parallel. The results are illustrated by means of a small simulation study and in the analysis of Canadian temperature data.
