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Sparse Sliced Inverse Regression

Summary: [This abstract is based on the authors' abstract.]Sliced inverse regression (SIR) is an effective method for dimension reduction and data visualization of high dimensional problems. However, because each SIR component is a linear combination of all the original predictors, it is often difficult to interpret the exact components. A new method, called sparse SIR, combines the shrinkage idea of the lasso with SIR to produce both accurate and sparse solutions. The proposed method is illustrated with simulation and a real data example.

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  • Topics:
  • Keywords: Inverse regression,Least squares,Regression analysis,Shrinkage
  • Author: Li, Lexin; Nachtsheim, Christopher J.
  • Journal: Technometrics