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Statistics in the big data era: Failures of the machine

Author

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  • Dunson, David B.

Abstract

There is vast interest in automated methods for complex data analysis. However, there is a lack of consideration of (1) interpretability, (2) uncertainty quantification, (3) applications with limited training data, and (4) selection bias. Statistical methods can achieve (1)-(4) with a change in focus.

Suggested Citation

  • Dunson, David B., 2018. "Statistics in the big data era: Failures of the machine," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 4-9.
    • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:4-9
    DOI: 10.1016/j.spl.2018.02.028
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    Citations

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    Cited by:

    1. Bowman, Adrian W., 2018. "Big questions, informative data, excellent science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 34-36.
    2. Paola Perchinunno & Massimo Bilancia & Domenico Vitale, 2021. "A Statistical Analysis of Factors Affecting Higher Education Dropouts," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 341-362, August.
    3. Chakraborty, Tanujit & Chakraborty, Ashis Kumar & Murthy, C.A., 2019. "A nonparametric ensemble binary classifier and its statistical properties," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 16-23.
    4. Sarah Friedrich & Gerd Antes & Sigrid Behr & Harald Binder & Werner Brannath & Florian Dumpert & Katja Ickstadt & Hans A. Kestler & Johannes Lederer & Heinz Leitgöb & Markus Pauly & Ansgar Steland & A, 2022. "Is there a role for statistics in artificial intelligence?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 823-846, December.
    5. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    6. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Borgonovo, Emanuele & Ghidini, Valentina & Hahn, Roman & Plischke, Elmar, 2023. "Explaining classifiers with measures of statistical association," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    8. Emanuele Aliverti & Kristian Lum & James E. Johndrow & David B. Dunson, 2021. "Removing the influence of group variables in high‐dimensional predictive modelling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 791-811, July.
    9. Torrecilla, José L. & Romo, Juan, 2018. "Data learning from big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 15-19.
    10. Olhede, Sofia C. & Wolfe, Patrick J., 2018. "The future of statistics and data science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 46-50.
    11. Shi, Wen & Chen, Xi, 2019. "Controlled Morris method: A new factor screening approach empowered by a distribution-free sequential multiple testing procedure," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 299-314.

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