A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
A type of nonlinear Fréchet regressions
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A differentially private framework for inference on Riemannian manifold data using tailored mechanisms for Fréchet mean and variance, with consistency, CLTs, and real-data demonstrations.
FRIDA is a proximal DC algorithm for signed Fréchet regression on complete Riemannian manifolds with two-sided bounded curvature, with proofs of minimizer existence, strong convexity of subproblems, and convergence.
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Differentially private inference framework for Riemannian manifold data
A differentially private framework for inference on Riemannian manifold data using tailored mechanisms for Fréchet mean and variance, with consistency, CLTs, and real-data demonstrations.
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Proximal DCA for Fr\'echet Regression on Riemannian Manifolds with Bounded Curvature
FRIDA is a proximal DC algorithm for signed Fréchet regression on complete Riemannian manifolds with two-sided bounded curvature, with proofs of minimizer existence, strong convexity of subproblems, and convergence.