A broad view of the nature and potential of computational information geometry in statistics
is offered. This new area suitably extends the manifold-based approach of classical …

This paper looks at boundary effects on inference in an important class of models including,
notably, logistic regression. Asymptotic results are not uniform across such models …

We give a personal view of what Information Geometry is, and what it is becoming, by
exploring a number of key topics: dual affine families, boundaries, divergences, tensorial …

Recent progress using geometry in the design of efficient Markov chain Monte Carlo
(MCMC) algorithms have shown the effectiveness of the Fisher Riemannian structure …

We introduce a new approach to goodness-of-fit testing in the high dimensional, sparse
extended multinomial context. The paper takes a computational information geometric …

In statistical practice model building, sensitivity and uncertainty are major concerns of the
analyst. This paper looks at these issues from an information geometric point of view. Here …

This paper applies the tools of computation information geometry [3]–in particular, high
dimensional extended multinomial families as proxies for the 'space of all distributions'–in …

This paper looks at effects, due to the boundary, on inference in logistic regression. It shows
that first--and, indeed, higher--order asymptotic results are not uniform across the model …

Geometry of Goodness-of-Fit Testing in High Dimensional Low Sample Size Modelling 0mm
Page 1 Introduction Pearson’s χ2 versus the deviance Other test statistics from power …