Minimax adaptive dimension reduction for regression

  title={Minimax adaptive dimension reduction for regression},
  author={Quentin Paris},
  journal={J. Multivariate Analysis},
In this paper, we address the problem of regression estimation in the context of a p-dimensional predictor when p is large. We propose a general model in which the regression function is a composite function. Our model consists in a nonlinear extension of the usual sufficient dimension reduction setting. The strategy followed for estimating the regression function is based on the estimation of a new parameter, called the reduced dimension. We adopt a minimax point of view and provide both lower… CONTINUE READING


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