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The 2016 Data Challenge of the American Statistical Association

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  • Roya Amjadi

    (Federal Highway Administration, Turner-Fairbank Highway Research Center, Office of Safety Research and Development)

  • Wendy Martinez

    (Office of Survey Methods Research, U.S. Bureau of Labor Statistics)

Abstract

The Sections on Statistical Graphics and Statistical Computing of the American Statistical Association have a long history of issuing Data Challenges with the first one starting in 1982/1983. The challenge is now an annual event where most of them use data collected and disseminated by the U.S. government. The data set for the 2016 Data Challenge was the Department of Transportation’s General Estimates System. The GES is collected by the National Highway Transportation Safety Administration and is a representative sample of police-reported motor vehicle crashes. This editorial introduces the five papers submitted by contestants in the data challenge.

Suggested Citation

  • Roya Amjadi & Wendy Martinez, 2021. "The 2016 Data Challenge of the American Statistical Association," Computational Statistics, Springer, vol. 36(3), pages 1553-1560, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-021-01076-5
    DOI: 10.1007/s00180-021-01076-5
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    References listed on IDEAS

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    1. Heike Hofmann & Hadley Wickham & Dianne Cook, 2019. "The 2013 Data Expo of the American Statistical Association," Computational Statistics, Springer, vol. 34(4), pages 1443-1447, December.
    2. Jonathan Auerbach & Christopher Eshleman & Rob Trangucci, 2021. "A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies," Computational Statistics, Springer, vol. 36(3), pages 1577-1604, September.
    3. Cody R. Philips & Robert C. Garrett & Alan J. Tatro & Thomas J. Fisher, 2021. "An analysis of crash-safety ratings and the true assessment of injuries by vehicle," Computational Statistics, Springer, vol. 36(3), pages 1639-1660, September.
    4. Dooti Roy & Ved Deshpande & M. Henry Linder, 2021. "A cluster-based taxonomy of bus crashes in the United States," Computational Statistics, Springer, vol. 36(3), pages 1621-1638, September.
    5. Gunes Alkan & Robert Farrow & Haichen Liu & Clayton Moore & Hon Keung Tony Ng & Lynne Stokes & Yihan Xu & Ziyuan Xu & Yuzhi Yan & Yifan Zhong, 2021. "Predictive modeling of maximum injury severity and potential economic cost in a car accident based on the General Estimates System data," Computational Statistics, Springer, vol. 36(3), pages 1561-1575, September.
    6. Dianne Cook, 2014. "The 2011 data Expo of the American Statistical Association," Computational Statistics, Springer, vol. 29(1), pages 117-119, February.
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    Cited by:

    1. Jonathan Auerbach & Christopher Eshleman & Rob Trangucci, 2021. "A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies," Computational Statistics, Springer, vol. 36(3), pages 1577-1604, September.
    2. Dooti Roy & Ved Deshpande & M. Henry Linder, 2021. "A cluster-based taxonomy of bus crashes in the United States," Computational Statistics, Springer, vol. 36(3), pages 1621-1638, September.

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