论文标题
在远程曲线时间序列中比较赫斯特指数估计器
A comparison of Hurst exponent estimators in long-range dependent curve time series
论文作者
论文摘要
赫斯特指数是自相似远程依赖随机过程的最简单数值摘要。我们考虑在远程依赖曲线时间序列中对Hurst指数的估计。我们的估计方法首先构建了长期协方差函数的估计值，我们通过动态功能主成分分析使用该估计，在估计跨越功能时间序列的主要子空间的正顺序函数时。在功能性回归分数集成的移动平均模型的背景下，我们比较了某些时间和频率域Hurst指数估计器中有限样本偏差，方差和均方根误差，并提出建议。
The Hurst exponent is the simplest numerical summary of self-similar long-range dependent stochastic processes. We consider the estimation of Hurst exponent in long-range dependent curve time series. Our estimation method begins by constructing an estimate of the long-run covariance function, which we use, via dynamic functional principal component analysis, in estimating the orthonormal functions spanning the dominant sub-space of functional time series. Within the context of functional autoregressive fractionally integrated moving average models, we compare finite-sample bias, variance and mean square error among some time- and frequency-domain Hurst exponent estimators and make our recommendations.
