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Flexible Heavy Tailed Distributions for Big Data

Author

Listed:
  • Yuanyuan Zhang

    (University of Manchester)

  • Saralees Nadarajah

    (University of Manchester)

Abstract

The Pareto type I distribution (also known as the power law distribution and Zipf’s law) appears to be the main distribution used to model heavy tailed phenomena in the big data literature. The Pareto type I distribution being one of the oldest heavy tailed distributions is not very flexible. Here, we show flexibility of four other heavy tailed distributions for modeling four big data sets in social networks. The Pareto type I distribution is shown not to provide the best or even an adequate fit for any of the data sets.

Suggested Citation

  • Yuanyuan Zhang & Saralees Nadarajah, 2017. "Flexible Heavy Tailed Distributions for Big Data," Annals of Data Science, Springer, vol. 4(3), pages 421-432, September.
    • Handle: RePEc:spr:aodasc:v:4:y:2017:i:3:d:10.1007_s40745-017-0113-4
    DOI: 10.1007/s40745-017-0113-4
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    References listed on IDEAS

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    1. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
    2. Barry C. Arnold, 2008. "Pareto and Generalized Pareto Distributions," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 7, pages 119-145, Springer.
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    Cited by:

    1. Chu, Jeffrey & Zhang, Yuanyuan & Chan, Stephen & Nadarajah, Saralees, 2020. "Bias reduction in the population size estimation of large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).

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