A new method for estimation and model selection: \(\rho\)-estimation
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Publication:510164
DOI10.1007/s00222-016-0673-5zbMath1373.62141arXiv1403.6057OpenAlexW4297632443MaRDI QIDQ510164
Lucien Birgé, Yannick Baraud, Mathieu Sart
Publication date: 16 February 2017
Published in: Inventiones Mathematicae (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1403.6057
Density estimation (62G07) Point estimation (62F10) Robustness and adaptive procedures (parametric inference) (62F35)
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Cites Work
- Unnamed Item
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- Robust estimation on a parametric model via testing
- Estimation of the transition density of a Markov chain
- Estimator selection with respect to Hellinger-type risks
- Robust linear least squares regression
- Stabilité et instabilité du risque minimax pour des variables indépendantes équidistribuées
- Risk bounds for statistical learning
- Asymptotic methods in statistical decision theory
- Asymptotics in statistics: some basic concepts
- Minimum contrast estimators on sieves: Exponential bounds and rates of convergence
- Risk bounds for model selection via penalization
- Rates of convergence for minimum contrast estimators
- Information-theoretic determination of minimax rates of convergence
- Concentration around the mean for maxima of empirical processes
- Convergence rates of posterior distributions.
- The method of sieves and minimum contrast estimators
- Weak convergence and empirical processes. With applications to statistics
- Minimal penalties for Gaussian model selection
- Local Rademacher complexities and oracle inequalities in risk minimization. (2004 IMS Medallion Lecture). (With discussions and rejoinder)
- Model selection via testing: an alternative to (penalized) maximum likelihood estimators.
- Concentration inequalities and asymptotic results for ratio type empirical processes
- Convergence of estimates under dimensionality restrictions
- Model selection for Poisson processes with covariates
- A note on bounds for VC dimensions
- Model selection for regression on a random design
- Maximum Likelihood: An Introduction
- [https://portal.mardi4nfdi.de/wiki/Publication:4743580 Approximation dans les espaces m�triques et th�orie de l'estimation]
- Robust Estimation of a Location Parameter
- On the Assumptions Used to Prove Asymptotic Normality of Maximum Likelihood Estimates
- Robust Statistics
