SPADES and mixture models

DOI10.1214/09-AOS790zbMath1198.62025arXiv0901.2044MaRDI QIDQ988014

Florentina Bunea, Marten H. Wegkamp, Alexandre B. Tsybakov, Adrian Barbu

Publication date: 24 August 2010

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0901.2044

zbMATH Keywords

aggregationadaptive estimationminimax risksparsitymixture modelsoracle inequalitiesnonparametric density estimationstatistical learninglassoconsistent model selectionpenalized least squares

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items

Supermix: sparse regularization for mixtures, Penalized logspline density estimation using total variation penalty, Adaptive log-density estimation, Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model, Solution of linear ill-posed problems using overcomplete dictionaries, Compressive Gaussian Mixture Estimation, Parameter recovery in two-component contamination mixtures: the \(L^2\) strategy, Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization, Adaptive Dantzig density estimation, High-dimensional additive hazards models and the lasso, Sparsity considerations for dependent variables, Optimal Kullback-Leibler aggregation in mixture density estimation by maximum likelihood, Penalized B-spline estimator for regression functions using total variation penalty, Proximal Distance Algorithms: Theory and Examples, Minimax bounds for Besov classes in density estimation, Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules, Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence, A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

Uses Software

• PDCO
• glmnet

Cites Work

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