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Contemporary Frontiers in High-Dimensional Statistical Data Analysis

7 January to 11 January 2008

Isaac Newton Institute for Mathematical Sciences, Cambridge, UK

Organisers: Professor David Banks (Duke ), Professor Michael Titterington (Glasgow ) and Professor Sara van de Geer (Zurich)

in association with the Newton Institute programme Statistical Theory and Methods for Complex, High-Dimensional Data (7 January to 27 June 2008)

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Accepted Posters:

Name Title Abstract Poster
Aldrin, M Improved predictions penalizing both slope and curvature in additive models Abstract
Chaudhuri, S Estimation of the minimum mea of normal populations under the tree order restriction Abstract
Cosma, I Dimension reduction and approximate clustering Abstract
Dolia, A The Kernel minimum volume covering ellipsoids Abstract
Dryden, I Shape analysis and molecule matching Abstract
Eldar, Y Uniformly improving maximum likelihood and the Cramer-Rao bound Abstract
Hamprecht, F A physically motivated approach to semi-supervised learning and a derived maximum expected impact active learning strategy Abstract
Harezlak, J Influence of the dependence structure of design matrices on the variable selection in high-dimensional regression problems Abstract
Jin, J Computation-based discovery of CIS regulatory modules by hidden Markov model Abstract
Koch, I Prediction with supervised independent component regression (joint work with Kanta Naito) Abstract
Kutyniok, G Sparse decompositions of anisotropic structures in high-dimensional data Abstract
Leng, C On variable selection via regularised rank regression Abstract
Lisboa, PJG Early stage exploration of high-dimensional data: clustering, visualisation and knowledge elicitation Abstract
Olhede, S Multiscale analysis of oceanic turbulence Abstract
Pan, J Variable selection in joint modelling of mean and covariance structures for longitudinal data Abstract
Reiss, P Functional generalised linear models with applications to neuroimaging Abstract
Schwartzman, A Empirical null and FDR inference for exponential families Abstract
Shi, JQ Curve prediction and clustering with mixtures of Gaussian process functional regression models Abstract
Vinciotti, V Quantile and M-quantile methods for gene expression data Abstract
Zhang, H Jointing edge and region-based information for geodesic active boundary finding in biomedical imagery Abstract

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