Estimating composite functions by model selection
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Publication:2438264
DOI10.1214/12-AIHP516zbMath1281.62093arXiv1102.2818MaRDI QIDQ2438264
Publication date: 10 March 2014
Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1102.2818
model selectionartificial neural networksGaussian mixturescurve estimationadaptationsingle index model
Density estimation (62G07) Nonparametric estimation (62G05) Learning and adaptive systems in artificial intelligence (68T05)
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Cites Work
- Unnamed Item
- Estimator selection with respect to Hellinger-type risks
- Nonparametric estimation of composite functions
- Gaussian model selection with an unknown variance
- Projection pursuit
- Risk bounds for model selection via penalization
- Approximation and estimation bounds for artificial neural networks
- Wavelet characterizations for anisotropic Besov spaces
- Model selection for Gaussian regression with random design
- Optimal global rates of convergence for nonparametric regression
- Adaptation to anisotropy and inhomogeneity via dyadic piecewise polynomial selection
- Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions
- Model selection via testing: an alternative to (penalized) maximum likelihood estimators.
- Multidimensional Spline Approximation
- Universal approximation bounds for superpositions of a sigmoidal function
- Model selection for (auto-)regression with dependent data
- A Projection Pursuit Algorithm for Exploratory Data Analysis
- A non asymptotic penalized criterion for Gaussian mixture model selection
- Gaussian model selection