6 papers
Distributionally Robust PCA with Data-Adaptive Wasserstein Geometry
Chuang Xu, Andrew T. A. Wood, Yanrong Yang
We develop a distributionally robust formulation of principal component analysis that minimizes worst-case reconstruction risk over distributions lying within a Wasserstein neighbo…
Robust Estimation of Location in Matrix Manifolds Using the Projected Frobenius Median
Houren Hong, Kassel Liam Hingee, Janice L. Scealy +1
We propose a robust method for location estimation in various matrix manifolds based on the projected Frobenius median, which is closely related to the spatial median. This method…
Equivalence Test for Mean Functions from Multi-population Functional Data
Chuang Xu, Andrew T. A. Wood, Yanrong Yang
Most existing methods for testing equality of means of functional data from multiple populations rely on assumptions of equal covariance and/or Gaussianity. In this work we provide…
Regression for spherical responses with linear and spherical covariates using a scaled link function
Shogo Kato, Kassel L. Hingee, Janice L. Scealy +1
We propose a regression model in which the responses are spherical variables and the covariates include linear and/or spherical variables. A novel link function is introduced by ex…
Principal Subsimplex Analysis
Hyeon Lee, Kassel Liam Hingee, Janice L. Scealy +3
Compositional data, which are data that lie in a simplex, naturally arise in many scientific domains such as geochemistry, microbiology, and economics. In such domains, obtaining s…
A Robust Extrinsic Single-index Model for Spherical Data
Houren Hong, Janice L. Scealy, Andrew T. A. Wood +1
Regression with a spherical response is challenging due to the absence of linear structure, making standard regression models inadequate. Existing methods, mainly parametric, lack…