DocumentCode :
926315
Title :
Absolute value optimization to estimate phase properties of stochastic time series (Corresp.)
Author :
Scargle, Jeffrey D.
Volume :
23
Issue :
1
fYear :
1977
fDate :
1/1/1977 12:00:00 AM
Firstpage :
140
Lastpage :
143
Abstract :
Most existing deconvolution techniques are incapable of determining phase properties of wavelets from time series data; to assure a unique solution, {em minimum phase} is usually assumed. It is demonstrated, for moving average processes of order one, that deconvolution filtering using the absolute value norm provides an estimate of the wavelet shape that has the correct phase character when the random driving process is nonnormal. Numerical tests show that this result probably applies to more general processes.
Keywords :
Autoregressive processes; Deconvolution; Moving-average processes; Phase estimation; Time series; Astrophysics; Autocorrelation; Deconvolution; Delay effects; Filters; Phase estimation; Radiofrequency interference; Random processes; Stochastic processes; Technological innovation;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.1977.1055668
Filename :
1055668
Link To Document :
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