Penalized orthogonal-components regression for large p small n data

@inproceedings{Zhang2009PenalizedOR,
  title={Penalized orthogonal-components regression for large p small n data},
  author={Dabao Zhang and Yanzhu Lin and Min Zhang},
  year={2009}
}
Here we propose a penalized orthogonal-components regression (POCRE) for large p small n data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlation to the response residuals. A new penalization framework, implemented via empirical Bayes thresholding, is presented to effectively identify sparse predictors of each component. POCRE is computationally efficient owing to its sequential construction of leading sparse principal components. In addition… CONTINUE READING

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