Spectral-norm risk rates for multi-taper estimation of Gaussian processes
José Luis Romero and
Michael Speckbacher
Journal of Nonparametric Statistics, 2022, vol. 34, issue 2, 448-464
Abstract:
We consider the estimation of the covariance of a stationary Gaussian process on a multi-dimensional grid from observations taken on a general acquisition domain. We derive spectral-norm risk rates for multi-taper estimators. When applied to one-dimensional acquisition intervals, these show that Thomson's classical multi-taper has optimal risk rates, as they match known benchmarks. We also extend existing lower risk bounds to multi-dimensional grids and conclude that multi-taper estimators associated with certain two-dimensional acquisition domains also have almost optimal risk rates.
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:34:y:2022:i:2:p:448-464
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DOI: 10.1080/10485252.2022.2071888
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