Estimating composite functions by model selection
| Autor: | Baraud, Yannick, Birgé, Lucien |
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| Rok vydání: | 2011 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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 $s$. Comment: 37 pages |
| Databáze: | arXiv |
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