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Conducting highly principled data science: A statistician’s job and joy

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  • Meng, Xiao-Li

Abstract

Highly Principled Data Science insists on methodologies that are: (1) scientifically justified; (2) statistically principled; and (3) computationally efficient. An astrostatistics collaboration, together with some reminiscences, illustrates the increased roles statisticians can and should play to ensure this trio, and to advance the science of data along the way.

Suggested Citation

  • Meng, Xiao-Li, 2018. "Conducting highly principled data science: A statistician’s job and joy," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 51-57.
    • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:51-57
    DOI: 10.1016/j.spl.2018.02.053
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    References listed on IDEAS

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    1. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
    2. Chan, Ngai Hang, 2001. "The Et Interview: Professor Joseph B. Kadane," Econometric Theory, Cambridge University Press, vol. 17(3), pages 633-668, June.
    3. Kastner, Gregor, 2016. "Dealing with Stochastic Volatility in Time Series Using the R Package stochvol," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i05).
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    Cited by:

    1. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.

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