Model selection for Gaussian regression with random design
From MaRDI portal
Publication:1763101
DOI10.3150/bj/1106314849zbMath1064.62030OpenAlexW4300464200MaRDI QIDQ1763101
Publication date: 21 February 2005
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3150/bj/1106314849
Related Items
SLOPE is adaptive to unknown sparsity and asymptotically minimax, Statistical estimation with model selection, Unnamed Item, Nearly optimal minimax estimator for high-dimensional sparse linear regression, Posterior contraction in Gaussian process regression using Wasserstein approximations, Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem, Estimating composite functions by model selection, Minimax bounds for Besov classes in density estimation
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Asymptotic methods in statistical decision theory
- Geometrizing rates of convergence. II
- Risk bounds for model selection via penalization
- Model selection in nonparametric regression
- Aggregating regression procedures to improve performance
- Combining different procedures for adaptive regression
- Model selection for regression on a fixed design
- Functional aggregation for nonparametric regression.
- Asymptotic equivalence theory for nonparametric regression with random design
- An adaptive compression algorithm in Besov spaces
- Model selection via testing: an alternative to (penalized) maximum likelihood estimators.
- Convergence of estimates under dimensionality restrictions
- Model selection for regression on a random design
- Ideal spatial adaptation by wavelet shrinkage
- An asymptotic property of model selection criteria
- Adaptive Regression by Mixing
- [https://portal.mardi4nfdi.de/wiki/Publication:4743580 Approximation dans les espaces m�triques et th�orie de l'estimation]
- Thresholding algorithms, maxisets and well-concentrated bases
- Gaussian model selection
