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Special Issue on Data-Adaptive Statistical Inference

Author

Listed:
  • Chambaz Antoine

    (Modal’X, Université Paris Ouest, Nanterre, France)

  • Hubbard Alan

    (Division of Biostatistics, University of California, Berkeley, CA, USA)

  • van der Laan Mark J.

    (Division of Biostatistics, University of California, Berkeley, CA, USA)

Abstract

No abstract is available for this item.

Suggested Citation

  • Chambaz Antoine & Hubbard Alan & van der Laan Mark J., 2016. "Special Issue on Data-Adaptive Statistical Inference," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 1-1, May.
    • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:1:p:1-1:n:20
    DOI: 10.1515/ijb-2016-0033
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    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Stijn Vansteelandt & Oliver Dukes, 2022. "Authors' reply to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 729-739, July.
    3. Youyi Fong & Yunda Huang & David Benkeser & Lindsay N. Carpp & Germán Áñez & Wayne Woo & Alice McGarry & Lisa M. Dunkle & Iksung Cho & Christopher R. Houchens & Karen Martins & Lakshmi Jayashankar & F, 2023. "Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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