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stat.ML2024
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…
math.ST2024
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…
stat.ME2024
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…