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文章基本信息

  • 标题:Low Assumptions, High Dimensions
  • 作者:Wasserman, Larry
  • 期刊名称:Rationality, Markets and Morals
  • 印刷版ISSN:1869-778X
  • 出版年度:2011
  • 卷号:2
  • 期号:49
  • 出版社:Frankfurt School Verlag, Frankfurt School of Finance & Management
  • 摘要: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.
  • 关键词:non partametric; distribution free; dimensionality
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