Estimating composite functions by model selection
Abstract
We consider the problem of estimating a function $s$ on $[-1,1]^{k}$ for large values of $k$ by looking for some best approximation by composite functions of the form $g\circ u$. Our solution is based on model selection and leads to a very general approach to solve this problem with respect to many different types of functions $g,u$ and statistical frameworks. In particular, we handle the problems of approximating $s$ by additive functions, single and multiple index models, neural networks, mixtures of Gaussian densities (when $s$ is a density) among other examples. We also investigate the situation where $s=g\circ u$ for functions $g$ and $u$ belonging to possibly anisotropic smoothness classes. In this case, our approach leads to a completely adaptive estimator with respect to the regularity of $s$.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2011
- DOI:
- 10.48550/arXiv.1102.2818
- arXiv:
- arXiv:1102.2818
- Bibcode:
- 2011arXiv1102.2818B
- Keywords:
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- Mathematics - Statistics Theory;
- 62G05
- E-Print:
- 37 pages