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Optimal prediction for kernel-based semi-functional linear regression
Keli Guo
,
Jun Fan
*
, Lixing Zhu
*
Corresponding author for this work
Department of Mathematics
Research output
:
Contribution to journal
›
Journal article
›
peer-review
7
Citations (Scopus)
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Dive into the research topics of 'Optimal prediction for kernel-based semi-functional linear regression'. Together they form a unique fingerprint.
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Keyphrases
Nonparametric Component
100%
Kernel-based
100%
Optimal Prediction
100%
Functional Linear Regression
100%
Functional Components
66%
Reproducing Kernel Hilbert Space
33%
Optimal Convergence Rate
33%
Penalized Least Squares Method
33%
Novel Prediction
33%
Prediction Approach
33%
Minimax Rate
33%
Semi-functional Linear Model
33%
Representer Theorem
33%
Minimax Optimal Rate
33%
Algorithm Efficiency
33%
Mathematics
Linear Regression Analysis
100%
Minimax
100%
Linear Models
50%
Numerical Analysis
50%
Hilbert Space
50%
Least Squares Method
50%
Rate of Convergence
50%