AI & ML
A Note on the Cut-Locus-Corrected Fr\'echet Mean Central Limit Theorem
arXiv stat.TH0 views
arXiv:2611.07956v1 Announce Type: new Abstract: We record two observations concerning \cite{HLW24}. First, the proof of its Theorem~1 yields the population linearization (13)--(14) at \textit{any critical point} of the Fr\'echet function $F_\mu$, under its remaining geometric and measure-theoretic assumptions; neither isolation nor a nonsingular Hessian of $F_\mu$ is needed. Second, its Theorem~2 is given a shorter proof by applying the M-estimator theorem \cite[Theorem~5.23, pp.~53--54]{vdV98} to negative squared distance in normal coordinates, as opposed to the Donsker argument used there. We verify the pointwise differentiability, local square-integrable Lipschitz bound, second order expansion of population loss, consistency, and empirical maximization hypotheses. The resulting covariance retains the cut-locus correction. The same argument applies to suitably selected local sample Fr\'echet minima when the population Hessian is positive definite.
Source:https://arxiv.org/abs/2611.07956PreviousSequential Certification of Partially Identified Product Targets: Information Limits and Adaptive SamplingNextExact regions for the symmetrized Blest coefficient
Related posts
There’s a New Way to Shop With AI. Text Alexa
0CNET News

13 waterproof jacket deals to save you from the November drizzle — up to 40% off leading brands
0TechRadar
In Vivo CRISPR Base Editing of <i>ANGPTL3</i> for Durable Lipid Lowering
0Nature News

Shure’s popular podcasting and streaming mic is almost $50 off
0The Verge

BoopFace: an MIT-licensed SVG robot avatar Web Component for AI agents
0dev.to

Written, translated into eleven languages, and seen by nobody
0dev.to