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Low Assumptions, High Dimensions

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  • Larry Wasserman

    ()
    (Carnegie Mellon University, Pittsburgh)

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    Abstract

    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.

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    Bibliographic Info

    Article provided by Frankfurt School Verlag, Frankfurt School of Finance & Management in its journal Rationality, Markets and Morals.

    Volume (Year): 2 (2011)
    Issue (Month): 49 (November)
    Pages:

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    Handle: RePEc:rmm:journl:v:2:y:2011:i:49

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    Keywords: non partametric; distribution free; dimensionality;

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