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文章基本信息

  • 标题:Iterative Optimal Sufficient Dimension Reduction for Conditional Mean in Multivariate Regression
  • 本地全文:下载
  • 作者:Jae Keun Yoo
  • 期刊名称:Journal of Data Science
  • 印刷版ISSN:1680-743X
  • 电子版ISSN:1683-8602
  • 出版年度:2009
  • 卷号:7
  • 期号:02
  • 页码:267-276
  • 出版社:Tingmao Publish Company
  • 摘要:

    Recently, Yoo and Cook (2007) developed an optimal version of Cook and Setodji (2003). When predictors are not highly skewed, the Yoo-Cook approach can be improved, especially with small samples, by iteratively estimating the inner product matrix used in their method without changing their asymptotic results. Since highly skewed predictors are often transformed for normality in sufficient dimension reduction literature, the proposed method can have more useful application in practice than Yoo and Cook (2007).

  • 关键词:Dimension;Reduction;Multivariate Regression;Inner Products;Asymptotic Results
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