Sufficient Dimension Reduction via Inverse Regression : A Minimum Discrepancy Approach

@inproceedings{Cook2005SufficientDR,
  title={Sufficient Dimension Reduction via Inverse Regression : A Minimum Discrepancy Approach},
  author={R. Dennis Cook and NI Liqiang},
  year={2005}
}
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimizing a quadratic objective function. An optimal member of this family, the inverse regression estimator (IRE), is proposed, along with inference methods and a computational algorithm. The IRE has at least three desirable properties: (1) Its estimated basis of the central dimension reduction subspace is asymptotically efficient, (2) its test statistic for dimension has an asymptotic chi-squared… CONTINUE READING
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