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 |
Information for Conference Participants | Local Information | Newton Institute Map | Statistical Theory and Methods for Complex, High Dimensional Data | Workshops | Newton Institute Home Page
