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Selection of variables and dimension reduction in high-dimensional non-parametric regression
- Source :
- Electronic Journal of Statistics 2008, Vol. 2, 1224-1241
- Publication Year :
- 2008
-
Abstract
- 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.<br />Comment: Published in at http://dx.doi.org/10.1214/08-EJS327 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
- Subjects :
- Mathematics - Statistics Theory
62G08 (Primary)
Subjects
Details
- Database :
- arXiv
- Journal :
- Electronic Journal of Statistics 2008, Vol. 2, 1224-1241
- Publication Type :
- Report
- Accession number :
- edsarx.0811.1115
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1214/08-EJS327