180 citations · 687 across the 34 of their papers we have counts for
12 papers · 1 filter
Estimating Maximal Symmetries of Regression Functions via Subgroup Lattices
Louis G. Christie, John A. D. Aston
We present a method for estimating the maximal symmetry of a continuous regression function. Knowledge of such a symmetry can be used to significantly improve modelling by removing…
Statistical Depth for Big Functional Data with Application to Neuroimaging
Alicia Nieto-Reyes, John A. D. Aston
Functional depth is the functional data analysis technique that orders a functional data set. Unlike the case of data on the real line, defining this order is non-trivial, and part…
Functional random effects modeling of brain shape and connectivity
Eardi Lila, John A. D. Aston
We present a statistical framework that jointly models brain shape and functional connectivity, which are two complex aspects of the brain that have been classically studied indepe…
Representation and reconstruction of covariance operators in linear inverse problems
Eardi Lila, Simon Arridge, John A. D. Aston
We introduce a framework for the reconstruction and representation of functions in a setting where these objects cannot be directly observed, but only indirect and noisy measuremen…
Statistical Analysis of Functions on Surfaces, with an application to Medical Imaging
Eardi Lila, John A. D. Aston
In Functional Data Analysis, data are commonly assumed to be smooth functions on a fixed interval of the real line. In this work, we introduce a comprehensive framework for the ana…
A Spatial Modeling Approach for Linguistic Object Data: Analysing dialect sound variations across Great Britain
Shahin Tavakoli, Davide Pigoli, John A. D. Aston +1
Dialect variation is of considerable interest in linguistics and other social sciences. However, traditionally it has been studied using proxies (transcriptions) rather than acoust…
Inference on covariance operators via concentration inequalities: k-sample tests, classification, and clustering via Rademacher complexities
Adam B. Kashlak, John A. D. Aston, Richard Nickl
We propose a novel approach to the analysis of covariance operators making use of concentration inequalities. First, non-asymptotic confidence sets are constructed for such operato…
The correlation space of Gaussian latent tree models and model selection without fitting
Nathaniel Shiers, Piotr Zwiernik, John A. D. Aston +1
We provide a complete description of possible covariance matrices consistent with a Gaussian latent tree model for any tree. We then present techniques for utilising these constrai…
Tests for separability in nonparametric covariance operators of random surfaces
John A. D. Aston, Davide Pigoli, Shahin Tavakoli
The assumption of separability of the covariance operator for a random image or hypersurface can be of substantial use in applications, especially in situations where the accurate…
Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach
Yan Zhou, Adam M Johansen, John A D Aston
Model comparison for the purposes of selection, averaging and validation is a problem found throughout statistics. Within the Bayesian paradigm, these problems all require the calc…
Dynamic filtering of static dipoles in magnetoencephalography
Alberto Sorrentino, Adam M. Johansen, John A. D. Aston +2
We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem…
Distributions associated with general runs and patterns in hidden Markov models
John A. D. Aston, Donald E. K. Martin
This paper gives a method for computing distributions associated with patterns in the state sequence of a hidden Markov model, conditional on observing all or part of the observati…