Establishes consistency and sharp rates of convergence for regularized M-estimators in RKHS via bias-variance decomposition with a novel complexity measure, including new rates for tensor product Sobolev spaces.
Splines Minimizing Rotation-Invariant Semi-Norms in
3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces penalized spline SCR models fitted via Laplace-approximate penalized marginal likelihood to flexibly model nonlinear covariate effects on density and approximate LGCP activity centre processes.
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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Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence
Establishes consistency and sharp rates of convergence for regularized M-estimators in RKHS via bias-variance decomposition with a novel complexity measure, including new rates for tensor product Sobolev spaces.
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Spatial Capture-Recapture With Penalized Regression Splines to Flexibly Model Wildlife Density and Distribution
Introduces penalized spline SCR models fitted via Laplace-approximate penalized marginal likelihood to flexibly model nonlinear covariate effects on density and approximate LGCP activity centre processes.
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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.