When it comes to estimating an unknown spectral density as simply and reliably as possible, parametric spectral density estimation using AR models and order selection via AIC is the method of choice. In contrast, no standard method has yet emerged for automatic nonparametric spectral density estimation, and there seems to be little willingness to weigh the advantages and disadvantages of different risk functions and the various methods for estimating them on a case-by-case basis, particularly because it is unclear whether the effort is even worthwhile without concrete prior information about the unknown spectral density. As a result, subjective visual methods are still widely used in practice to determine the appropriate smoothing parameter for a nonparametric estimation. This article aims to encourage the increased use of objective automatic methods by presenting evidence that using what is arguably the simplest and most straightforward frequency-domain version of the AIC for the automatic determination of an appropriate bandwidth enables results that are comparable to those obtained using the standard parametric approach. This evidence is based on both real-world time series and synthetic time series with spectral densities of varying complexity.

翻译：在尽可能简单可靠地估计未知谱密度时，使用AR模型并通过AIC进行阶数选择的参数谱密度估计是首选方法。相比之下，目前尚未有标准方法用于自动非参数谱密度估计，而且似乎很少有人愿意逐一权衡不同风险函数及其估计方法的优缺点，尤其是在没有关于未知谱密度的具体先验信息时，尚不清楚这种努力是否值得。因此，在实践中，主观的视觉方法仍被广泛用于确定非参数估计的合适平滑参数。本文旨在通过展示使用一种可以说是最简单、最直接的频率域AIC版本来自动确定适当带宽，能够获得与标准参数方法相媲美的结果，从而推动客观自动方法的更广泛应用。这一证据基于真实时间序列和具有不同复杂程度谱密度的合成时间序列。