In:
INFORMS Journal on Computing, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 24, No. 1 ( 2012-02), p. 42-52
Kurzfassung:
In this paper, we propose an algorithm to fit heavy-tailed (HT) distribution functions by generalized hyperexponential (GH) distribution functions. A discussion of the steps, usage, and accuracy of the GH algorithm is given. Several examples in this paper show that the proposed method can be applied to fit HT distributions with a completely monotone probability density function (pdf) very well, like the Pareto distribution and the Weibull distribution with the shape parameter less than one, as well as HT distributions whose pdf is not completely monotone, like the lognormal distribution. In addition, we provide an example that shows that the proposed method can be applied to density estimation of real data presenting a heavy tail.
Materialart:
Online-Ressource
ISSN:
1091-9856
,
1526-5528
DOI:
10.1287/ijoc.1100.0443
Sprache:
Englisch
Verlag:
Institute for Operations Research and the Management Sciences (INFORMS)
Publikationsdatum:
2012
ZDB Id:
2070411-2
ZDB Id:
2004082-9
SSG:
3,2
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