Optimal Design of Experiments on Riemannian Manifolds

DOI10.1080/01621459.2022.2146587MaRDI QIDQ6567882

Hang Li, Enrique Del Castillo

Publication date: 5 July 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• General equivalence theory for optimum designs (approximate theory)
• Model-oriented design of experiments
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• On active learning methods for manifold data
• Spatial regression models over two-dimensional manifolds
• The Equivalence of Two Extremum Problems
• Response Surfaces, Mixtures, and Ridge Analyses
• A Ridge-Type Procedure for Design of Experiments
• Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
• Nonparametric Regression for Spherical Data
• Principal Boundary on Riemannian Manifolds
• Optimal Design of Experiments
• Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
• Local Linear Regression on Manifolds and Its Geometric Interpretation
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• Learning Theory
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
• The Sequential Generation of $D$-Optimum Experimental Designs
• Theory of Reproducing Kernels

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