6 citations · 15 across the 9 of their papers we have counts for
10 papers
Nonlinear Causal Discovery via Kernel Anchor Regression
Wenqi Shi, Wenkai Xu
Learning causal relationships is a fundamental problem in science. Anchor regression has been developed to address this problem for a large class of causal graphical models, though…
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
Moritz Weckbecker, Wenkai Xu, Gesine Reinert
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may…
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds
Wenkai Xu, Takeru Matsuda
In many applications, we encounter data on Riemannian manifolds such as torus and rotation groups. Standard statistical procedures for multivariate data are not applicable to such…
A Stein Goodness of fit Test for Exponential Random Graph Models
Wenkai Xu, Gesine Reinert
We propose and analyse a novel nonparametric goodness of fit testing procedure for exchangeable exponential random graph models (ERGMs) when a single network realisation is observe…
A kernel test for quasi-independence
Tamara Fernández, Wenkai Xu, Marc Ditzhaus +1
We consider settings in which the data of interest correspond to pairs of ordered times, e.g, the birth times of the first and second child, the times at which a new user creates a…
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez, Nicolas Rivera, Wenkai Xu +1
Survival Analysis and Reliability Theory are concerned with the analysis of time-to-event data, in which observations correspond to waiting times until an event of interest such as…