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 the infinite-dimensional case, challeng...

Mathematical scientists of diverse backgrounds are being asked to apply the techniques of their specialty to data which is greater in both size and complexity than that which has been studied previously. Large, high-dimensional data sets, for which traditional linear methods are inadequate, pose challenges in representation, visualization, interpretation and analysis. A common finding is that t...

Computational linguistics studies the computational processes involved in language learning, production, and comprehension. Computational linguists believe that the essence of these processes (in humans and machines) is a computational manipulation of information. Computational psycholinguistics studies psychological aspects of human language (e.g., the time course of sentence comprehension) in...

In this work we propose a novel unsupervised classification technique based on the estimation of nonlinear nonparametric mixed-effects models. The proposed method is an iterative algorithm that alternates a nonparametric EM step and a nonlinear Maximum Likelihood step. We apply this new procedure to perform an unsupervised clustering of longitudinal data in two different case studies.

Intelligent multimedia systems require cross-media decision mechanisms for integrating pieces of information expressed by different modalities. However, one still strives to find a cross-media relations model that will be general enough to describe how different verbal and non-verbal modalities interact with one another and that will also have computational applicability. In this paper, we expl...

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Matrix trace inequalities are finding increased use in many areas such as analysis, where they can be used to generalise several well known classical inequalities, and computational statistics, where they can be applied, for example, to data fitting problems. In this paper we give simple proofs of two useful matrix trace inequalities and provide applications to orthogonal regression and matrix ...

Latent class (LC) analysis is used to construct empirical evidence on the existence of latent subgroups based on the associations among a set of observed discrete variables. One of the tests used to infer about the number of underlying subgroups is the bootstrap likelihood ratio test (BLRT). Although power analysis is rarely conducted for this test, it is important to identify, clarify, and spe...

Similarly to the previous ERCIM WG workshops we plan several plenary lectures and specialized sessions devoted to different topics from both computational statistics and numerical

Anand Banerjee, Alexander Berzhkovskii, and Ralph Nossal Program in Physical Biology, Eunice Kennedy Shriver National Institute of Child Health, and Human Development, National Institutes of Health, Bethesda, Maryland 20892, USA Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Mar...