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Article Dans Une Revue The international journal of biostatistics Année : 2016

Data-Adaptive Statistical Inference: foreword to the DGIJB special issue

Antoine Chambaz
Alan Hubbard
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Mark J van Der Laan
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Résumé

The concomitant emergence of big data, explosion of ubiquitous computational resources and democratization of the access to more powerful computing make it necessary and possible to rethink pragmatically the practice of statistics. While numerous machine learning methods provide much ever easier access to data-mining tools and sophisticated prediction, there is a growing realization that ad hoc and non-prespecified approaches to high-dimensional problems lend themselves to a proliferation of ``findings'' of dubious reproducibility. This period of fast-paced evolution is thus a blessing for statistics. It is a golden opportunity to build upon more than a century of methodological research in statistics and five decades of methodological research in machine learning to bend the course of statistics in a new direction, away from the misuse of parametric models and reporting of non-robust inference, to tackle rigorously the challenges that we, as a community, are confronted with. We asked researchers currently engaged in cutting edge research on data-adaptive inferential methods to share their views with us. The result is a compelling collection of advances in statistical theory and practice.
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Dates et versions

hal-01340095 , version 1 (12-07-2016)

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  • HAL Id : hal-01340095 , version 1

Citer

Antoine Chambaz, Alan Hubbard, Mark J van Der Laan. Data-Adaptive Statistical Inference: foreword to the DGIJB special issue. The international journal of biostatistics, 2016, 12 (1), pp.1. ⟨hal-01340095⟩
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