Statistical Theory and Methods for Complex, High-Dimensional Data
Created: | 2008-02-01 14:48 |
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Institution: | Isaac Newton Institute for Mathematical Sciences |
Editors' group: | SMS Editors group for the Newton Institute |
Description: | Most of twentieth-century statistical theory was restricted to problems in which the number p of 'unknowns', such as parameters, is much less than n, the number of experimental units. However, the practical environment has changed dramatically over the last twenty years or so, with the spectacular evolution of computing facilities and the emergence of applications in which the number of experimental units is comparatively small but the underlying dimension is massive, leading to the desire to fit complex models for which the effective p is very large. Areas of application include image analysis, microarray analysis, finance, document classification, astronomy and atmospheric science. Some methodological advances have been made, but there is a need to provide firm consolidation in the form of a systematic and critical assessment of the new approaches as well as appropriate theoretical underpinning in this 'large p, small n' context. The existence of key applications strongly motivates the programme, but the fundamental aim is to promote core theoretical and methodological research. Both frequentist and Bayesian paradigms will be featured. The programme is directed at a broad research community, including both mainstream statisticians and the growing population of researchers in machine learning. The methodological issues likely to be covered fall roughly into four overlapping categories:
* strategies for explicit and implicit dimension-reduction, including latent-structure methods, semiparametric models and large-scale multiple testing; * classification methods for complex datasets, including machine-learning methods such as support vector machines; * asymptotics for increasing dimension, including the application of random matrix theory to high-dimensional multivariate methods; * graphical and other visualisation methods for complex datasets. EVENTS: - Contemporary Frontiers in High-Dimensional Statistical Data Analysis http://www.newton.ac.uk/programmes/SCH/schw01.html - High Dimensional Statistics in Biology http://www.newton.ac.uk/programmes/SCH/schw02.html - Inference and Estimation in Probabilistic Time-Series Models http://www.newton.ac.uk/programmes/SCH/schw05.html - Future Directions in High-Dimensional Data Analysis http://www.newton.ac.uk/programmes/SCH/schw03.html |
Media items
This collection contains 129 media items.
Media items
A Bayesian method for non-Gaussian autoregressive quantile function time series models
61,343 views
Cai, Y (Plymouth)
Wednesday 18 June 2008, 15:30-16:10
Inference and Estimation in Probabilistic Time-Series Models
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 24 Jun 2008
A Bayesian probabilistic approach to transform public microarray repositories into disease diagnosis databases
386 views
Huang, H (UC Berkeley)
Friday 04 April 2008, 14:00-15:00
High Dimensional Statistics in Biology
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 11 Apr 2008
A database of foreign exchange deals
410 views
Clarkson, P (BNP Paribas)
Thursday 31 January 2008, 11:00-12:00
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Sun 3 Feb 2008
A geometric perspective on learning theory and algorithms
1,799 views
Niyogi, P (Chicago)
Thursday 10 January 2008, 16:30-17:30
Contemporary Frontiers in High-Dimensional Statistical Data Analysis
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 22 Jan 2008
A methodological framework for Monte Carlo estimation of continuous-time processes
375 views
Papaspiliopoulos, O (Universitat Pompeu Fabra)
Friday 20 June 2008, 14:00-15:00
Inference and Estimation in Probabilistic Time-Series Models
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 27 Jun 2008
A modern perspective on auxiliary particle filters
490 views
Whiteley, N (Cambridge)
Wednesday 18 June 2008, 16:50-17:30
Inference and Estimation in Probabilistic Time-Series Models
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 24 Jun 2008
A physicist's approach to high-dimensional inference
376 views
Hoyle, D (Manchester)
Friday 11 January 2008, 14:00-15:00
Contemporary Frontiers in High-Dimensional Statistical Data Analysis
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 23 Jan 2008
Adaptive Monte Carlo Markov Chains
527 views
Moulines, E (CNRS)
Friday 20 June 2008, 11:30-12:30
Inference and Estimation in Probabilistic Time-Series Models
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 27 Jun 2008
Analysis of graphs using diffusion processes and random walks (a random walk through spectral graph theory)
757 views
Hancock, E (York)
Tuesday 18 March 2008, 11:00-12:00
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 26 Mar 2008
Applications of approximate inference and experimental design for sparse (generalised) linear models
382 views
Seeger, MW (MPI for Biological Cybernetics)
Friday 27 June 2008, 11:30-12:30
Future Directions in High-Dimensional Data Analysis
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 15 Jul 2008
Approximate genealogical inference
350 views
McVean, G (Oxford)
Friday 04 April 2008, 10:00-11:00
High Dimensional Statistics in Biology
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 15 Apr 2008
Approximate Inference for Continuous Time Markov Processes
274 views
Opper, M (Technische Universität Berlin)
Thursday 19 June 2008, 11:30-12:30
Inference and Estimation in Probabilistic Time-Series Models
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 25 Jun 2008
Approximation of functional spatial regression models using bivariate splines
422 views
Guillas, S (University College London)
Thursday 05 June 2008, 11:00-12:00
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 17 Jun 2008
Assessing high-dimensional latent variable models
1,607 views
Murray, I (Toronto)
Thursday 15 May 2008, 11:00-12:00
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 28 May 2008
Bayesian Gaussian process models for multi-sensor time-series prediction
350 views
Roberts, S (Oxford)
Thursday 19 June 2008, 17:00-17:30
Inference and Estimation in Probabilistic Time-Series Models
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 26 Jun 2008
Bayesian hierarchical clustering
590 views
Heller, K (UCL)
Monday 18 February 2008, 15:00-15:30
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 21 Feb 2008
Bayesian nonparametric latent feature models
571 views
Ghahramani, Z (Cambridge)
Monday 18 February 2008, 15:30-16:00
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 21 Feb 2008
Bootstrap and parametric inference: successes and challenges
446 views
Young, A (Imperial)
Monday 07 January 2008, 15:30-16:30
Contemporary Frontiers in High-Dimensional Statistical Data Analysis
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 17 Jan 2008
Breakdown point of model selection when the number of variables exceeds the number of observations
2,011 views
Donoho, D (Stanford)
Monday 07 January 2008, 10:00-11:00
Contemporary Frontiers in High-Dimensional Statistical Data Analysis
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 16 Jan 2008
Challenge of dimensionality in model selection and classification
440 views
Fan, J (Princeton)
Tuesday 08 January 2008, 15:30-16:30
Contemporary Frontiers in High-Dimensional Statistical Data Analysis
Collection: Statistical Theory and Methods for Complex, High-Dimensional Data
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 18 Jan 2008