In the choice of an estimator for the spectrum of a stationary time
series from a finite sample of the process, the problems of bias
control and consistency, or "smoothing," are dominant. In this paper
we present a new method based on a "local" eigenexpansion to estimate
the spectrum in terms of the solution of an integral equation. Computationally
this method is equivalent to using the weishted average of a series
of direct-spectrum estimates based on orthogonal data windows (discrete
prolate spheroidal sequences) to treat both the bias and smoothing
problems. Some of the attractive features of this estimate are: there
are no arbitrary windows; it is a small sample theory; it is consistent;
it provides an analysis-of-variance test for line components; and
it has high resolution. We also show relations of this estimate to
maximum-likelihood estimates, show that the estimation capacity of
the estimate is high, and show applications to coherence and polyspectrum
estimates.
%0 Journal Article
%1 thomson:1982
%A Thomson, D. J.
%D 1982
%J Proceedings of the IEEE
%K geophysics seismics seismology
%N 9
%P 1055--1096
%R 10.1109/PROC.1982.12433
%T Spectrum estimation and harmonic analysis
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1456701
%V 70
%X In the choice of an estimator for the spectrum of a stationary time
series from a finite sample of the process, the problems of bias
control and consistency, or "smoothing," are dominant. In this paper
we present a new method based on a "local" eigenexpansion to estimate
the spectrum in terms of the solution of an integral equation. Computationally
this method is equivalent to using the weishted average of a series
of direct-spectrum estimates based on orthogonal data windows (discrete
prolate spheroidal sequences) to treat both the bias and smoothing
problems. Some of the attractive features of this estimate are: there
are no arbitrary windows; it is a small sample theory; it is consistent;
it provides an analysis-of-variance test for line components; and
it has high resolution. We also show relations of this estimate to
maximum-likelihood estimates, show that the estimation capacity of
the estimate is high, and show applications to coherence and polyspectrum
estimates.
@article{thomson:1982,
abstract = {In the choice of an estimator for the spectrum of a stationary time
series from a finite sample of the process, the problems of bias
control and consistency, or "smoothing," are dominant. In this paper
we present a new method based on a "local" eigenexpansion to estimate
the spectrum in terms of the solution of an integral equation. Computationally
this method is equivalent to using the weishted average of a series
of direct-spectrum estimates based on orthogonal data windows (discrete
prolate spheroidal sequences) to treat both the bias and smoothing
problems. Some of the attractive features of this estimate are: there
are no arbitrary windows; it is a small sample theory; it is consistent;
it provides an analysis-of-variance test for line components; and
it has high resolution. We also show relations of this estimate to
maximum-likelihood estimates, show that the estimation capacity of
the estimate is high, and show applications to coherence and polyspectrum
estimates.},
added-at = {2012-09-01T13:08:21.000+0200},
author = {Thomson, D. J.},
biburl = {https://www.bibsonomy.org/bibtex/2a2a87f8a67b4599e5d86ac7431339085/nilsma},
doi = {10.1109/PROC.1982.12433},
interhash = {e5f8b19431c1db3cb81aee39dce7b908},
intrahash = {a2a87f8a67b4599e5d86ac7431339085},
issn = {0018-9219},
journal = {Proceedings of the IEEE},
keywords = {geophysics seismics seismology},
number = 9,
pages = {1055--1096},
timestamp = {2021-02-09T13:26:17.000+0100},
title = {Spectrum estimation and harmonic analysis},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1456701},
volume = 70,
year = 1982
}