On a nonlinear extension of the principal fitted component model
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Publication:6168914
DOI10.1016/j.csda.2023.107707MaRDI QIDQ6168914
Kyongwon Kim, Jun Song, Jae Keun Yoo
Publication date: 11 July 2023
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
reproducing kernel Hilbert spacesufficient dimension reductionprincipal component modelprincipal fitted component model
Cites Work
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- Fisher lecture: Dimension reduction in regression
- Principal support vector machines for linear and nonlinear sufficient dimension reduction
- Principal fitted components for dimension reduction in regression
- A general theory for nonlinear sufficient dimension reduction: formulation and estimation
- Nonlinear sufficient dimension reduction for functional data
- Dimension reduction for functional data based on weak conditional moments
- On post dimension reduction statistical inference
- Defining probability density for a distribution of random functions
- Contour regression: a general approach to dimension reduction
- On Directional Regression for Dimension Reduction
- Sliced Inverse Regression for Dimension Reduction
- Likelihood-Based Sufficient Dimension Reduction
- Combining eigenvalues and variation of eigenvectors for order determination
- OUP accepted manuscript
- Dimension reduction in regression without matrix inversion
- Theory of Reproducing Kernels
- Comment
- Some properties of Gaussian reproducing kernel Hilbert spaces and their implications for function approximation and learning theory
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