Dimension Reduction for Nonelliptically Distributed Predictors

@inproceedings{Li2009DimensionRF,
  title={Dimension Reduction for Nonelliptically Distributed Predictors},
  author={Bing Li and Yuexiao Dong},
  year={2009}
}
Sufficient dimension reduction methods often require stringent conditions on the joint distribution of the predictor, or, when such conditions are not satisfied, rely on marginal transformation or reweighting to fulfill them approximately. For example, a typical dimension reduction method would require the predictor to have elliptical or even multivariate normal distribution. In this paper, we re-formulate the commonly used dimension reduction methods, via the notion of " central solution space… CONTINUE READING

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