An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.
Statistical Modelling , volume =
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A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
citing papers explorer
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Infinite-Dimensional Spherical Kernel ridge Regression
An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.
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Variable Domain Multivariate Functional Principal Component Analysis
A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.
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Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.