Statistical Theory and Methods for Complex, High-Dimensional Data

Statistical Theory and Methods for Complex, High-Dimensional Data's image
Created: 2008-02-01 14:48
Institution: Isaac Newton Institute for Mathematical Sciences
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.

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Media items

A Bayesian method for non-Gaussian autoregressive quantile function time series models

   61,335 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

   384 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

   402 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

   1,699 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

   371 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

   486 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

   375 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

   526 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


Applications of approximate inference and experimental design for sparse (generalised) linear models

   380 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

   347 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

   421 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

   1,605 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

   345 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

   585 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

   563 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

   441 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

   1,995 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


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