Selection of variables and dimension reduction in high-dimensional non-parametric regression

Details Selection of variables and dimension reduction in high-dimensional non-parametric regression Selection of variables and dimension reduction in high-dimensional non-parametric regression

2008-11-07

arxiv.org/abs/0811.1115v2

Electronic Journal of Statistics 2008, Vol. 2, 1224-1241

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/08-EJS327 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by t

Scientific paper

10.1214/08-EJS327

We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on the number of observations). Estimation of a $\beta$-regular regression function $f$ cannot be faster than the slow rate $n^{-2\beta/(2\beta+d)}$. Hopefully, in some situations, $f$ depends only on a few numbers of the coordinates of $X$. In this paper, we construct two procedures. The first one selects, with high probability, these coordinates. Then, using this subset selection method, we run a local polynomial estimator (on the set of interesting coordinates) to estimate the regression function at the rate $n^{-2\beta/(2\beta+d^*)}$, where $d^*$, the "real" dimension of the problem (exact number of variables whom $f$ depends on), has replaced the dimension $d$ of the design. To achieve this result, we used a $l_1$ penalization method in this non-parametric setup.

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