activity
20112024
most citedSticky central limit theorems on open books

56 citations · 112 across the 10 of their papers we have counts for

collaborators

11 papers

math.ST2024

Empirical likelihood for Fréchet means on open books

Karthik Bharath, Huiling Le, Andrew T A Wood +1

Empirical Likelihood (EL) is a type of nonparametric likelihood that is useful in many statistical inference problems, including confidence region construction and -sample probl…

math.PR2022

Central limit theorem for intrinsic Frechet means in smooth compact Riemannian manifolds

Thomas Hotz, Huiling Le, Andrew T. A. Wood

We prove a central limit theorem (CLT) for the Frechet mean of independent and identically distributed observations in a compact Riemannian manifold assuming that the population Fr…

math.PR2020★ 8 cited

A diffusion approach to Stein's method on Riemannian manifolds

Huiling Le, Alexander Lewis, Karthik Bharath +1

We detail an approach to develop Stein's method for bounding integral metrics on probability measures defined on a Riemannian manifold . Our approach exploits the relati…

stat.ME2019★ 4 cited

Principal nested shape space analysis of molecular dynamics data

Ian L. Dryden, Kwang-Rae Kim, Charles A. Laughton +1

Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of interest to summarize the shape evolution of the molecules in a succinct, low-dime…

stat.ME2018★ 7 cited

Smoothing splines on Riemannian manifolds, with applications to 3D shape space

Kwang-Rae Kim, Ian L. Dryden, Huiling Le +1

There has been increasing interest in statistical analysis of data lying in manifolds. This paper generalizes a smoothing spline fitting method to Riemannian manifold data based on…

math.PR2017★ 8 cited

The Logarithm Map, its Limits and Frechet Means in Orthant Spaces

Dennis Barden, Huiling Le

The first part of the paper studies the expression for, and the properties of, the logarithm map on an orthant space, which is a simple stratified space, with the aim of analysing…