Ramdom sampling and the Grassberger-Procaccia algorithm

G. Kember; A. C. Fowler

doi:10.1016/0375-9601(92)90683-D

Ramdom sampling and the Grassberger-Procaccia algorithm

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Abstract

The correlation integral of a data set is determined from information based upon a random sample of it. Given a data set of a certain size, we use sampling statistics to specify the sample size required to infer the correlation integral to a desired accuracy. The minimum sample size required to resolve a correlation exponent is found by consideration of both data set and sampling errors.

Original language	English
Pages (from-to)	429-432
Number of pages	4
Journal	Physics Letters A
Volume	161
Issue number	5
DOIs	https://doi.org/10.1016/0375-9601(92)90683-D
Publication status	Published - 13 Jan 1992
Externally published	Yes

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title = "Ramdom sampling and the Grassberger-Procaccia algorithm",

abstract = "The correlation integral of a data set is determined from information based upon a random sample of it. Given a data set of a certain size, we use sampling statistics to specify the sample size required to infer the correlation integral to a desired accuracy. The minimum sample size required to resolve a correlation exponent is found by consideration of both data set and sampling errors.",

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AB - The correlation integral of a data set is determined from information based upon a random sample of it. Given a data set of a certain size, we use sampling statistics to specify the sample size required to infer the correlation integral to a desired accuracy. The minimum sample size required to resolve a correlation exponent is found by consideration of both data set and sampling errors.

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Ramdom sampling and the Grassberger-Procaccia algorithm

Abstract

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