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An asymptotically minimax kernel machine

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Publication:464460
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DOI10.1016/j.spl.2014.08.005zbMath1366.68238OpenAlexW2073622351MaRDI QIDQ464460

Debashis Ghosh

Publication date: 27 October 2014

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2014.08.005


zbMATH Keywords

decision theorydata miningnonparametric regressionsupport vector machineshard-thresholding


Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05)




Cites Work

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  • Minimax bounds for sparse PCA with noisy high-dimensional data
  • Optimal filtering of square-integrable signals in Gaussian noise
  • Minimax risk over \(l_ p\)-balls for \(l_ q\)-error
  • The asymptotic distribution of REML estimators
  • Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction
  • Maximum likelihood estimation of models for residual covariance in spatial regression
  • Updating the Inverse of a Matrix
  • Design-adaptive Nonparametric Regression
  • Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
  • Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models
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