activity
20042023
most citedA data-based power transformation for compositional data

36 citations · 109 across the 14 of their papers we have counts for

collaborators

14 papers

stat.CO2023★ 1 cited

A branch cut approach to the probability density and distribution functions of a linear combination of central and non-central Chi-square random variables

Alfred Kume, Tomonari Sei, Andrew T. A. Wood

The paper considers the distribution of a general linear combination of central and non-central chi-square random variables by exploring the branch cut regions that appear in the s…

stat.ME2023

Robust score matching for compositional data

Janice L. Scealy, Kassel L. Hingee, John T. Kent +1

The restricted polynomially-tilted pairwise interaction (RPPI) distribution gives a flexible model for compositional data. It is particularly well-suited to situations where some o…

stat.ME2023

Generalized Score Matching

Jiazhen Xu, Janice L. Scealy, Andrew T. A. Wood +1

Score matching is an estimation procedure that has been developed for statistical models whose probability density function is known up to proportionality but whose normalizing con…

stat.ME2022

Cauchy robust principal component analysis with applications to high-deimensional data sets

Ayisha Fayomi, Yannis Pantazis, Michail Tsagris +1

Principal component analysis (PCA) is a standard dimensionality reduction technique used in various research and applied fields. From an algorithmic point of view, classical PCA ca…

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.ST2022★ 2 cited

Generalized Score Matching for Regression

Jiazhen Xu, Janice L. Scealy, Andrew T. A. Wood +1

Many probabilistic models that have an intractable normalizing constant may be extended to contain covariates. Since the evaluation of the exact likelihood is difficult or even imp…