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Absolute value optimization to estimate phase properties of stochastic time seriesMost existing deconvolution techniques are incapable of determining phase properties of wavelets from time series data; to assure a unique solution, 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.
Document ID
19770044507
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Scargle, J. D.
(NASA Ames Research Center Theoretical and Planetary Studies Branch, Moffett Field, Calif., United States)
Date Acquired
August 8, 2013
Publication Date
January 1, 1977
Publication Information
Publication: IEEE Transactions on Information Theory
Volume: IT-23
Subject Category
Cybernetics
Accession Number
77A27359
Funding Number(s)
CONTRACT_GRANT: NSF GU-3162
Distribution Limits
Public
Copyright
Other

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