Bias correction of quadratic spectral estimators
File(s)
Accepted version
Author(s)
Astfalck, Lachlan C
Sykulski, Adam M
Cripps, Edward J
Type
Journal Article
Abstract
The three cardinal, statistically consistent, families of nonparametric estimators to the power spectral density of a time series are lag-window, multitaper and Welch estimators. However, when estimating power spectral densities from a finite sample each can be subject to nonignorable bias. astfalck2024debiasing developed a method that offers significant bias reduction for finite samples for Welch’s estimator, which this article extends to the larger family of quadratic estimators, thus offering similar theory for bias correction of lag-window and multitaper estimators as well as combinations thereof. Importantly, this theory may be used in conjunction with any and all tapers and lag-sequences designed for bias reduction, and so should be seen as an extension to valuable work in these fields, rather than a supplanting methodology. The order of computation is larger than O (n log n) which is typical in spectral analyses, but not insurmountable in practice. Simulation studies support the theory with comparisons across variations of quadratic estimators.
Date Issued
2025-04-28
Date Acceptance
2025-04-07
Citation
Biometrika, 2025, 112 (3)
ISSN
0006-3444
Publisher
Oxford University Press
Journal / Book Title
Biometrika
Volume
112
Issue
3
Copyright Statement
© The Author(s) 2025. Published by Oxford University Press on behalf of Biometrika Trust. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Publication Status
Published online
Date Publish Online
2025-04-28