Algorithm for adaptively smoothing the log-periodogram
Copyright policy ) Neagu, Radu; Zurbenko, Igor;
Algorithm for adaptively smoothing the log-periodogram
It is shown that for a short-memory stationary Gaussian process, under some mathematical conditions, the distribution of the average of log-transformed periodogram can be approximated by the normal distribution. This result is used to develop an algorithm, based on the minimum cross-entropy, for adaptive smoothing.
- University at Albany, State University of New York United States
- General Electric (United States) United States
- State University of New York at Potsdam United States
normal distribution, adaptive smoothing, Data smoothing in stochastic control theory, cross-entropy, Approximations to statistical distributions (nonasymptotic), average of log-transformed periodogram
normal distribution, adaptive smoothing, Data smoothing in stochastic control theory, cross-entropy, Approximations to statistical distributions (nonasymptotic), average of log-transformed periodogram
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selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 4
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