The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Introduction to Nonsmooth Analysis and Optimization
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Functions that are not differentiable in the classical sense have become a central tool in modern mathematical models for imaging, inverse problems, machine learning, and optimal control of differential equations. These models are increasingly formulated in infinite-dimensional function spaces to be independent of problem size and discretization quality. This book presents a unified and rigorous introduction to the infinite-dimensional analysis and algorithmic solution of nonsmooth optimization problems arising from the above-mentioned models, from the necessary theoretical tools of nonsmooth analysis to state-of-the-art algorithms and their convergence and stability analysis.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Derives pointwise bound |u| ≤ C ρ1^{-τ} near Γ for -Δ_{p(x)}u + |u|^{q-1}u=0 to prove singularity removability and p→1 convergence to -Δ_1 u + |u|^{q-1}u=0 in BV.
citing papers explorer
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Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
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Pointwise Estimates Near Singular Sets for Quasilinear Elliptic Equations
Derives pointwise bound |u| ≤ C ρ1^{-τ} near Γ for -Δ_{p(x)}u + |u|^{q-1}u=0 to prove singularity removability and p→1 convergence to -Δ_1 u + |u|^{q-1}u=0 in BV.