Emergent Mind
Adaptive Kernel Estimation of the Spectral Density with Boundary Kernel Analysis
(1803.03906)
Published Mar 11, 2018
in
stat.ME
,
cs.CV
,
eess.AS
,
eess.SP
,
math.ST
,
and
stat.TH
Abstract
A hybrid estimator of the log-spectral density of a stationary time series is proposed. First, a multiple taper estimate is performed, followed by kernel smoothing the log-multitaper estimate. This procedure reduces the expected mean square error by $({\pi2 \over 4}){.8}$ over simply smoothing the log tapered periodogram. The optimal number of tapers is $O(N{8/15})$. A data adaptive implementation of a variable bandwidth kernel smoother is given. When the spectral density is discontinuous, one sided smoothing estimates are used.
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