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Journals and Conferences
Exponential families and the related exponential dispersion models are the workhorses of parametric modelling theory. One reason for their popularity is their associated inference theory which is very clean both from a theoretical and a computational point of view. One way in which this set of tools can be enriched in a natural and interpretable way is… (More)
A new class of local mixture models called local scale mixture models is introduced here. This class is particularly suitable for the analysis of mixtures of the negative exponential distribution. The affine structure revealed by specific asymptotic expansions is the motivation for the construction of these models. They are shown to have very nice… (More)
Melatonin is a major endocrine product of the pineal gland. It is produced at night when noradrenaline acts on beta-adrenergic receptors to stimulate enzymes which catalyse the formation of melatonin from serotonin. It is believed by some that nocturnal melatonin levels reflect beta-receptor function. The melatonin rhythm is also thought to be an indication… (More)
In this study, we discuss a decision theoretic or fully Bayesian approach to the sample size question in clinical trials with binary responses. Data are assumed to come from two binomial distributions. A Dirichlet distribution is assumed to describe prior knowledge of the two success probabilities p1 and p2. The parameter of interest is p = p1 - p2. The… (More)
In this article, we discuss an optimization approach to the sample size question, founded on maximizing the value of information in comparison studies with binary responses. The expected value of perfect information (EVPI) is calculated and the optimal sample size is obtained by maximizing the expected net gain of sampling (ENGS), the difference between the… (More)
This paper lays the foundations for a new framework for numerically and computationally applying information geometric methods to statistical modelling.
This paper lays the foundations for a unified framework for numerically and computationally applying methods drawn from a range of currently distinct geometrical approaches to statistical modelling. In so doing, it extends information geometry from a manifold based approach to one where the simplex is the fundamental geometrical object, thereby allowing… (More)
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 information geometry to a simplicial setting, in order to obtain an operational universal model space. Additional underlying theory and illustrative real examples are presented. In… (More)
This paper applies the tools of computation information geometry  – in particular, high dimensional extended multinomial families as proxies for the ‘space of all distributions’ – in the inferentially demanding area of statistical mixture modelling. A range of resultant benefits are noted.