7 papers
Post-reduction inference for confidence sets of models
Heather Battey, Daniel Garcia Rasines, Yanbo Tang
Sparsity in a regression context makes the model itself an object of interest, pointing to a confidence set of models as the appropriate presentation of evidence. A difficulty in a…
Non-standard boundary behaviour in two-component mixture models
Heather Battey, Peter McCullagh, Daniel Xiang
Consider a binary mixture model of the form , where is standard Gaussian and is a completely specified heavy-tailed distribution with the same sup…
Regression graphs and sparsity-inducing reparametrizations
Jakub Rybak, Heather Battey, Karthik Bharath
That parametrization and sparsity are inherently linked raises the possibility that relevant models, not obviously sparse in their natural formulation, exhibit a population-level s…
On the role of parametrization in models with a misspecified nuisance component
Heather Battey, Nancy Reid
The paper is concerned with inference for a parameter of interest in models that share a common interpretation for that parameter but that may differ appreciably in other respects.…
Cox reduction and confidence sets of models: a theoretical elucidation
R. M. Lewis, H. S. Battey
For sparse high-dimensional regression problems, Cox and Battey [1, 9] emphasised the need for confidence sets of models: an enumeration of those small sets of variables that fit t…
Inference in High-dimensional Linear Regression
Heather S. Battey, Nancy Reid
This paper develops an approach to inference in a linear regression model when the number of potential explanatory variables is larger than the sample size. The approach treats eac…
An Unethical Optimization Principle
Nicholas Beale, Heather Battey, Anthony C. Davison +1
If an artificial intelligence aims to maximise risk-adjusted return, then under mild conditions it is disproportionately likely to pick an unethical strategy unless the objective f…