Cet article traite du problème de l’estimation d’une fonction définie sur lorsque est grand en utilisant des approximations de par des fonctions composées de la forme . Notre solution est fondée sur la sélection de modèle et conduit, pour résoudre ce problème, à une approche très générale tant sur les possibilités de choix des fonctions et que sur les cadres statistiques d’application. En particulier, et entre autres exemples, nous considérons l’approximation de par des fonctions additives, des modèles de type “single” ou “multiple index”, des réseaux de neurones, ou des mélanges de densités gaussiennes lorsque est elle-même une densité. Nous étudions également le cas où est exactement de la forme pour des fonctions et appartenant à des classes de régularités qui peuvent être anisotropes. Dans ce cas, notre approche conduit à un estimateur complètement adaptatif par rapport aux régularités de et .
We consider the problem of estimating a function on for large values of by looking for some best approximation of by composite functions of the form . 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 and statistical frameworks. In particular, we handle the problems of approximating by additive functions, single and multiple index models, artificial neural networks, mixtures of Gaussian densities (when is a density) among other examples. We also investigate the situation where for functions and belonging to possibly anisotropic smoothness classes. In this case, our approach leads to a completely adaptive estimator with respect to the regularities of and .
@article{AIHPB_2014__50_1_285_0, author = {Baraud, Yannick and Birg\'e, Lucien}, title = {Estimating composite functions by model selection}, journal = {Annales de l'I.H.P. Probabilit\'es et statistiques}, volume = {50}, year = {2014}, pages = {285-314}, doi = {10.1214/12-AIHP516}, mrnumber = {3161532}, zbl = {1281.62093}, language = {en}, url = {http://dml.mathdoc.fr/item/AIHPB_2014__50_1_285_0} }
Baraud, Yannick; Birgé, Lucien. Estimating composite functions by model selection. Annales de l'I.H.P. Probabilités et statistiques, Tome 50 (2014) pp. 285-314. doi : 10.1214/12-AIHP516. http://gdmltest.u-ga.fr/item/AIHPB_2014__50_1_285_0/
[1] Adaptation to anisotropy and inhomogeneity via dyadic piecewise polynomial selection. Math. Methods Statist. 21 (2012) 1-28. | MR 2901269
.[2] Estimator selection with respect to Hellinger-type risks. Probab. Theory Related Fields 151 (2011) 353-401. | MR 2834722 | Zbl pre05968717
.[3] Model selection for (auto-)regression with dependent data. ESAIM Probab. Stat. 5 (2001) 33-49. | Numdam | MR 1845321 | Zbl 0990.62035
, and .[4] Gaussian model selection with an unknown variance. Ann. Statist. 37 (2009) 630-672. | MR 2502646 | Zbl 1162.62051
, and .[5] Risk bounds for model selection via penalization. Probab. Theory Related Fields 113 (1999) 301-413. | MR 1679028 | Zbl 0946.62036
, and .[6] Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inform. Theory 39 (1993) 930-945. | MR 1237720 | Zbl 0818.68126
.[7] Approximation and estimation bounds for artificial neural networks. Machine Learning 14 (1994) 115-133. | Zbl 0818.68127
.[8] Model selection via testing: An alternative to (penalized) maximum likelihood estimators. Ann. Inst. Henri Poincaré Probab. Stat. 42 (2006) 273-325. | Numdam | MR 2219712 | Zbl pre05024238
.[9] Model selection for Poisson processes. In Asymptotics: Particles, Processes and Inverse Problems, Festschrift for Piet Groeneboom 32-64. E. Cator, G. Jongbloed, C. Kraaikamp, R. Lopuhaä and J. Wellner (Eds). IMS Lecture Notes - Monograph Series 55. Inst. Math. Statist., Beachwood, OH, 2007. | MR 2459930 | Zbl 1176.62082
.[10] Model selection for density estimation with -loss. Probab. Theory Related Fields. To appear. Available at http://arxiv.org/abs/1102.2818. | Zbl 1285.62037
.[11] Gaussian model selection. J. Eur. Math. Soc. (JEMS) 3 (2001) 203-268. | MR 1848946 | Zbl 1037.62001
and .[12] Multidimensional spline approximation. SIAM J. Numer. Anal. 17 (1980) 380-402. | MR 581486 | Zbl 0437.41010
, and .[13] Constructive Approximation. Springer, Berlin, 1993. | MR 1261635 | Zbl 0797.41016
and .[14] A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. C-23 (1974) 881-890. | Zbl 0284.68079
and .[15] Wavelet characterizations for anisotropic Besov spaces. Appl. Comput. Harmon. Anal. 12 (2002) 179-208. | MR 1884234 | Zbl 1003.42024
.[16] Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions. Ann. Statist. 35 (2007) 2589-2619. | MR 2382659 | Zbl 1129.62034
and .[17] Projection pursuit (with discussion). Ann. Statist. 13 (1985) 435-525. | MR 790553 | Zbl 0595.62059
.[18] Nonparametric estimation of composite functions. Ann. Statist. 37 (2009) 1360-1404. | MR 2509077 | Zbl 1160.62030
, and .[19] A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM Probab. Stat. 15 (2011) 41-68. | Numdam | MR 2870505 | Zbl pre06157507
and .[20] Optimal global rates of convergence for nonparametric regression. Ann. Statist. 10 (1982) 1040-1053. | MR 673642 | Zbl 0511.62048
.