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Big data and biostatistics: The death of the asymptotic Valhalla

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  • Wit, Ernst C.

Abstract

Despite the ubiquity of Big Data in the modern scientific discourse, most references describe storage and query considerations and rarely full-flexed analyses. In this article, we propose another definition with particular relevance to biometrics. We argue that the complexity of the generating measure of biological process means that the model complexity of any statistical model will have to be smaller. Only, when the model is used for prediction can we have any hope that the number of available features reasonably outnumbers the desired complexity of the model.

Suggested Citation

  • Wit, Ernst C., 2018. "Big data and biostatistics: The death of the asymptotic Valhalla," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 30-33.
    • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:30-33
    DOI: 10.1016/j.spl.2018.02.039
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    References listed on IDEAS

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    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Ernst Wit & Edwin van den Heuvel & Jan-Willem Romeijn, 2012. "‘All models are wrong...’: an introduction to model uncertainty," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 217-236, August.
    3. Luigi Augugliaro & Angelo M. Mineo & Ernst C. Wit, 2016. "A differential-geometric approach to generalized linear models with grouped predictors," Biometrika, Biometrika Trust, vol. 103(3), pages 563-577.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    5. Luigi Augugliaro & Angelo M. Mineo & Ernst C. Wit, 2013. "Differential geometric least angle regression: a differential geometric approach to sparse generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 471-498, June.
    6. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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