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Newton InstituteWeb SeminarsProgrammes & Workshops2008SCH > SCHW01

7 - 11 Jan 2008

Contemporary Frontiers in High-Dimensional Statistical Data Analysis

in association with the Statistical Theory and Methods for Complex, High-Dimensional Data programme

Warning: Information on this page may be out of date.

For the full list of seminars given during this programme, see:

7 Jan 2008: Breakdown point of model selection when the number of variables exceeds the number of observations
D. Donoho
7 Jan 2008: The deterministic lasso
S. van de Geer
7 Jan 2008: Methods for visualizing high dimensional data
E. Wegman
7 Jan 2008: Bootstrap and parametric inference: successes and challenges
A. Young
8 Jan 2008: Practical and information-theoretic limitations in high-dimensional inference
M. Wainwright
8 Jan 2008: Some thoughts on nonparametric classification: nearest neighbours, bagging and max likelihood estimation of shape-constrained densities
R. Samworth
8 Jan 2008: Model-based sufficient dimension reduction for regression
RD. Cook
8 Jan 2008: Kernel-based contrast functions for sufficient dimension reduction
M. Jordan
8 Jan 2008: Challenge of dimensionality in model selection and classification
J. Fan
8 Jan 2008: Regularised estimation of high dimensional covariance matrices
P. Bickel
9 Jan 2008: The ultrametric topology perspective on analysis of massive, very high dimensional data stores
F. Murtagh
9 Jan 2008: P-values for computer-intensive classifiers
L. Duembgen
9 Jan 2008: Nonparametric cluster analysis: estimating the cluster tree of a density
W. Stuetzle
9 Jan 2008: Sparsity modelling in large-scale dynamic models for portfolio analysis
M. West
9 Jan 2008: Computationally tractable statistical estimation when there are more variables than observations
E. Candes
9 Jan 2008: Learning in high dimensions, noise, sparsity and treelets
B. Nadler
10 Jan 2008: Estimating a response parameter in missing data models with high-dimensional covariates
AW. van der Vaart
10 Jan 2008: Persistence: alternative proofs of some results of Greenshtein and Ritov
J. Wellner
10 Jan 2008: Looking at models in high-dimensional data spaces
D. Cook
10 Jan 2008: The surprising structure of Gaussian point clouds and its implications for signal processing
J. Tanner
10 Jan 2008: Finding low-dimensional structure in high-dimensional data
A. Lee
10 Jan 2008: A geometric perspective on learning theory and algorithms
P. Niyogi
11 Jan 2008: High-dimensional variable selection and graphs: sparsity, faithfulness and stability
P. Buehlmann
11 Jan 2008: Time series regression with semiparametric factor dynamics
E. Mammen
11 Jan 2008: Using side information for prediction
B Y. Yu
11 Jan 2008: A physicist's approach to high-dimensional inference
D. Hoyle
11 Jan 2008: Models, model lists, model spaces and predictive optimality
B. Clarke

Newton InstituteWeb SeminarsProgrammes & Workshops2008SCH > SCHW01

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