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On a heuristic point of view concerning the citation distribution: introducing the Wakeby distribution.

Katchanov YL, Markova YV - Springerplus (2015)

Bottom Line: The Wakeby distribution is derived in the paper from the simple and general inhomogeneous Choquet-Deny convolution equation for a non-probability measure.We give statistical evidence that the Wakeby distribution is a reasonable approximation of the empirical citation distributions.AMS Subject Classification: 91D30; 91D99.

View Article: PubMed Central - PubMed

Affiliation: National Research University Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000 Russian Federation.

ABSTRACT
The paper proposes a heuristic approach to modeling the cumulative distribution of citations of papers in scientific journals by means of the Wakeby distribution. The Markov process of citation leading to the Wakeby distribution is analyzed using the terminal time formalism. The Wakeby distribution is derived in the paper from the simple and general inhomogeneous Choquet-Deny convolution equation for a non-probability measure. We give statistical evidence that the Wakeby distribution is a reasonable approximation of the empirical citation distributions. AMS Subject Classification: 91D30; 91D99.

No MeSH data available.


Probability – Probability plot ofZ for Dataset 1. Distribution: GPD.
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Fig2: Probability – Probability plot ofZ for Dataset 1. Distribution: GPD.

Mentions: Best-fit PDs for both data sets were performed using the Mathwave EasyFit 2014 data analysis software. The 63 PDs were automatically fitted to the empirical distributions of the data sets. The Kolmogorov–Smirnov test and the Anderson–Darling test were performed to assess goodness-of-fit, and the PDs were ranked according to the goodness-of-fit. The values of the test statistics for the top 5 PDs are reported in Tables 1 and 2 (see also Figures 1, 2, 3 and 4).Figure 1


On a heuristic point of view concerning the citation distribution: introducing the Wakeby distribution.

Katchanov YL, Markova YV - Springerplus (2015)

Probability – Probability plot ofZ for Dataset 1. Distribution: GPD.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4352413&req=5

Fig2: Probability – Probability plot ofZ for Dataset 1. Distribution: GPD.
Mentions: Best-fit PDs for both data sets were performed using the Mathwave EasyFit 2014 data analysis software. The 63 PDs were automatically fitted to the empirical distributions of the data sets. The Kolmogorov–Smirnov test and the Anderson–Darling test were performed to assess goodness-of-fit, and the PDs were ranked according to the goodness-of-fit. The values of the test statistics for the top 5 PDs are reported in Tables 1 and 2 (see also Figures 1, 2, 3 and 4).Figure 1

Bottom Line: The Wakeby distribution is derived in the paper from the simple and general inhomogeneous Choquet-Deny convolution equation for a non-probability measure.We give statistical evidence that the Wakeby distribution is a reasonable approximation of the empirical citation distributions.AMS Subject Classification: 91D30; 91D99.

View Article: PubMed Central - PubMed

Affiliation: National Research University Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000 Russian Federation.

ABSTRACT
The paper proposes a heuristic approach to modeling the cumulative distribution of citations of papers in scientific journals by means of the Wakeby distribution. The Markov process of citation leading to the Wakeby distribution is analyzed using the terminal time formalism. The Wakeby distribution is derived in the paper from the simple and general inhomogeneous Choquet-Deny convolution equation for a non-probability measure. We give statistical evidence that the Wakeby distribution is a reasonable approximation of the empirical citation distributions. AMS Subject Classification: 91D30; 91D99.

No MeSH data available.