Future Directions in High-Dimensional Data Analysis: New Methodologies: New Data Types and New Applications

23 June to 27 June 2008

Isaac Newton Institute for Mathematical Sciences, Cambridge, UK

Organisers: Dr David Barber (University College London), Professor Iain Johnstone (Stanford University), Dr Richard Samworth (University of Cambridge) and Professor Michael Titterington (University of Glasgow)

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 Contributed Talks:

Name Title Abstract
Airoldi, E The exchangeable graph model for statistical network analysis Abstract
Helland, IS Optimal prediction from relevant components Abstract
Koch, I Dimension selection with independent component analysis and its application to prediction Abstract
Li, L Model free variable selection via sufficient dimension reduction Abstract
Pan, G Limiting theorems for large dimensional sample means, sample covariance matrices and Hotelling's T2 statistics Abstract
Shi, JQ Generalised gaussian process functional regression model Abstract
Wang, Y Estimation of large volatility matrix for high-frequency financial data Abstract
West, M High-dimensional adaptive mixture modelling: Challenges in problems in the "large n, large k and moderate p" paradigm Abstract
Xing, E Statistical network analysis and inference: methods and applications Abstract

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