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Low Assumptions, High Dimensions


  • Larry Wasserman

    () (Carnegie Mellon University, Pittsburgh)


These days, statisticians often deal with complex, high dimensional datasets. Researchers in statistics and machine learning have responded by creating many new methods for analyzing high dimensional data. However, many of these new methods depend on strong assump-tions. The challenge of bringing low assumption inference to high dimensional settings requires new ways to think about the foundations of statistics. Traditional foundational concerns, such as the Bayesian versus frequentist debate, have become less important.

Suggested Citation

  • Larry Wasserman, 2011. "Low Assumptions, High Dimensions," Rationality, Markets and Morals, Frankfurt School Verlag, Frankfurt School of Finance & Management, vol. 2(49), November.
  • Handle: RePEc:rmm:journl:v:2:y:2011:i:49

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    References listed on IDEAS

    1. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    2. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, June.
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