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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
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 Institute
>
Web Seminars
>
Programmes & Workshops
>
2008
>
SCH
> SCHW01
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