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31 papers · 1 filter
A flexible class of latent variable models for the analysis of antibody response data
Emanuele Giorgi, Jonas Wallin
Existing approaches to modelling antibody concentration data are mostly based on finite mixture models that rely on the assumption that individuals can be divided into two distinct…
Comment on arXiv:2202.01553: The Distribution of a Gaussian Covariate Statistic
Joe Whittaker
This is a comment on arXiv:2202.01553. In regression Gaussian covariate p-values (Davies and D{ü}mbgen, arXiv:2202.01553) are used to control greedy forward subset selection by acc…
Neural Bayes estimation and selection for complex bivariate extremal dependence models
Lídia M. André, Jennifer L. Wadsworth, Raphaël Huser
Likelihood-free approaches are appealing for performing inference on complex dependence models, either because it is not possible to formulate a likelihood function, or its evaluat…
Large multi-response linear regression estimation based on low-rank pre-smoothing
Xinle Tian, Alex Gibberd, Matthew Nunes +1
Pre-smoothing is a technique aimed at increasing the signal-to-noise ratio in data to improve subsequent estimation and model selection in regression problems. However, pre-smoothi…
Treatment-control comparisons in platform trials including non-concurrent controls
Marta Bofill Roig, Pavla Krotka, Katharina Hees +5
Shared controls in platform trials comprise concurrent and non-concurrent controls. For a given experimental arm, non-concurrent controls refer to data from patients allocated to t…
Inference for bivariate extremes via a semi-parametric angular-radial model
Callum John Rowlandson Murphy-Barltrop, Ed Mackay, Philip Jonathan
The modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide var…