A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model-Reference-Cited by-同舟云学术

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

Published:2020-12 Issue: Volume:152 Page:107039
ISSN:0167-9473
Container-title:Computational Statistics & Data Analysis
language:en
Short-container-title:Computational Statistics & Data Analysis

Author:

Lv Shaogao,Fan Zengyan,Lian Heng^ORCID,Suzuki Taiji,Fukumizu Kenji

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Statistics and Probability

Reference46 articles.

1. Theory of reproducing kernels;Aronszajn;Trans. Amer. Math. Soc.,1950

2. Consistency of the group Lasso and multiple kernel learning;Bach;J. Mach. Learn. Res.,2008

3. Convex optimization with sparsity-inducing norms;Bach,2011

4. Local Rademacher complexities;Bartlett;Ann. Statist.,2005

5. Rademacher and Gaussian complexities: risk bounds and structural results;Bartlett;J. Mach. Learn. Res.,2002

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1. Linear regression models with multiplicative distortions under new identifiability conditions;Statistica Neerlandica;2023-05-24

2. Stability and approximation of solutions in new reproducing kernel Hilbert spaces on a semi‐infinite domain;Mathematical Methods in the Applied Sciences;2021-05-31

3. Measuring the symmetry of model errors for varying coefficient regression models based on correlation coefficient;Communications in Statistics - Simulation and Computation;2021-03-16

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