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Loss Data Analytics

Author

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  • Edward Frees

    (for the Actuarial Community)

Abstract

Loss Data Analytics is an interactive, online, freely available text. The idea behind the name Loss Data Analytics is to integrate classical loss data models from applied probability with modern analytic tools. In particular, we seek to recognize that big data (including social media and usage based insurance) are here and high speed computation is readily available. The online version contains many interactive objects (quizzes, computer demonstrations, interactive graphs, video, and the like) to promote deeper learning. A subset of the book is available for offline reading in pdf and EPUB formats. The online text will be available in multiple languages to promote access to a worldwide audience.

Suggested Citation

  • Edward Frees, 2018. "Loss Data Analytics," Papers 1808.06718, arXiv.org.
    • Handle: RePEc:arx:papers:1808.06718
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1808.06718
    File Function: Latest version
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    References listed on IDEAS

    as
    1. Tse,Yiu-Kuen, 2009. "Nonlife Actuarial Models," Cambridge Books, Cambridge University Press, number 9780521764650, December.
    2. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    3. McDonald, James B. & Xu, Yexiao J., 1995. "A generalization of the beta distribution with applications," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 133-152.
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    Cited by:

    1. Fadal A.A. Aldhufairi & Jungsywan H. Sepanski, 2020. "New families of bivariate copulas via unit weibull distortion," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-20, December.

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