A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model

DOI10.1016/j.csda.2020.107039OpenAlexW3036059775MaRDI QIDQ830540

Zengyan Fan, Kenji Fukumizu, Heng Lian, Taiji Suzuki, Shao-Gao Lv

Publication date: 7 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2020.107039

zbMATH Keywords

varying coefficient modelssparsityhigh dimensionsstructure learningreproducing kernel Hilbert space (RKHS)

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Measuring the symmetry of model errors for varying coefficient regression models based on correlation coefficient

Cites Work

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• Fast learning rate of multiple kernel learning: trade-off between sparsity and smoothness
• Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data
• Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators
• Probability in Banach spaces. Isoperimetry and processes
• Radial kernels and their reproducing kernel Hilbert spaces
• Sparsity in multiple kernel learning
• Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models
• Kernel methods in machine learning
• Statistical estimation in varying coefficient models
• A Bennett concentration inequality and its application to suprema of empirical processes
• Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space
• Sparse high-dimensional varying coefficient model: nonasymptotic minimax study
• Simultaneous analysis of Lasso and Dantzig selector
• Learning rates of least-square regularized regression
• Local Rademacher complexities
• Optimization with Sparsity-Inducing Penalties
• On varying-coefficient independence screening for high-dimensional varying-coefficient models
• Learning Theory
• Support Vector Machines
• Varying-coefficient models and basis function approximations for the analysis of repeated measurements
• 10.1162/153244303321897690
• Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Shrinkage Estimation of the Varying Coefficient Model
• Minimax-optimal rates for sparse additive models over kernel classes via convex programming
• Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration
• Theory of Reproducing Kernels
• Compressed sensing
• New concentration inequalities in product spaces

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