5 papers
Generalized network autoregressive modelling of longitudinal networks with application to presidential elections in the USA
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Longitudinal networks are becoming increasingly relevant in the study of dynamic processes characterised by known or inferred community structure. Generalised Network Autoregressiv…
The MAPS Algorithm: Fast model-agnostic and distribution-free prediction intervals for supervised learning
Daniel Salnikov, Dan Leonte, Kevin Michalewicz
A fundamental problem in modern supervised learning is computing reliable conditional prediction intervals in high-dimensional settings: existing methods often rely on restrictive…
Concentration inequalities for the sample correlation coefficient
Daniel Salnikov
The sample correlation coefficient plays an important role in many statistical analyses. We study the moments of under the bivariate Gaussian model assumption, provide a no…
Modelling clusters in network time series with an application to presidential elections in the USA
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Network time series are becoming increasingly relevant in the study of dynamic processes characterised by a known or inferred underlying network structure. Generalised Network Auto…
New tools for network time series with an application to COVID-19 hospitalisations
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Network time series are becoming increasingly important across many areas in science and medicine and are often characterised by a known or inferred underlying network structure, w…