7 papers
Stein's method in network analysis
A. D. Barbour, Adrian Fischer, Gesine Reinert
The paper consists of a brief survey of the use of Stein's method in network analysis. Topics covered include normal and Poisson approximation of graph--based statistics, approxima…
TASTE: Task-Aware Out-of-Distribution Detection via Stein Operators
Michał Kozyra, Gesine Reinert
Out-of-distribution detection methods are often either data-centric, detecting deviations from the training input distribution irrespective of their effect on a trained model, or m…
Stein's method of moment estimators for local dependency exponential random graph models
Adrian Fischer, Gesine Reinert, Wenkai Xu
Providing theoretical guarantees for parameter estimation in exponential random graph models is a largely open problem. While maximum likelihood estimation has theoretical guarante…
A Bayesian mixture model for Poisson network autoregression
Elly Hung, Anastasia Mantziou, Gesine Reinert
In this paper, we propose a new Bayesian Poisson network autoregression mixture model (PNARM). Our model combines ideas from the models of Dahl 2008, Ren et al. 2024 and Armillotta…
COVID-19 incidence in the Republic of Ireland: A case study for network-based time series models
Stephanie Armbruster, Gesine Reinert
The generalised network autoregressive (GNAR) model conceptualises time series on the vertices of a network; it has an autoregressive component for temporal dependence and a spatia…
Stein's density method for multivariate continuous distributions
Guillaume Mijoule, Martin Raič, Gesine Reinert +1
This paper provides a general framework for Stein's density method for multivariate continuous distributions. The approach associates to any probability density function a canonica…
A two-component copula with links to insurance
Samiha Ismail, Gao Yu, Gesine Reinert +1
This paper presents a new copula to model dependencies between insurance entities, by considering how insurance entities are affected by both macro and micro factors. The model use…