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 …

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 …

We take a very high level overview of the relationship between Geometry and Applied
Statistics 50 years from the birth of Information Geometry. From that date we look both …

Empirical likelihood and the bootstrap play influential roles in contemporary statistics. This
thesis studies two distinct statistical inference problems, referred to as Part I and Part II …

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 …

Local mixture models give an inferentially tractable but still flexible alternative to general
mixture models. Their parameter space naturally includes boundaries; near these the …

Abstract This paper uses Information Geometry in a practical and important applied
statistical context: Cox regression in survival analysis. We explore the geometry of the …

Geometric properties of statistical models and their influence on statistical inference and
asymptotic theory reveal the profound relationship between geometry and statistics. This …