Computational information geometry: model sensitivity and approximate cuts

Joint work with Karim Anaya-Izquierdo (Open University), Paul Marriott (University of Waterloo) and Paul Vos (East Carolina).

Our project uses computational information geometry to develop diagnostic tools to help understand sensitivity to model choice by building an appropriate perturbation space for a given inference problem. Focused on generalised linear models, the long-term aim is to engage with statistical practice via appropriate R software encapsulating the underlying geometry, whose details the user may ignore.

This talk focuses on the role played by the concept of an approximate cut. Amongst other features, the perturbation space thereby constructed allows us to expose the differences in inferences resulting from different models, albeit that they may be equally well supported by the data. A running example illustrates the development.

EPSRC support under grant EP/E017878/1 is gratefully acknowledged.