EE-SRI: Prediction of correlation structure from large random matrices
Event details
| Date | 21.06.2012 |
| Hour | 16:15 › 17:15 |
| Speaker | Alfred Hero, University of Michigan, Ann Arbor |
| Location |
Room MXF1
|
| Category | Conferences - Seminars |
Abstract
Random matrices arise in many areas of engineering, social sciences, and natural sciences. For example, when rows of the random matrix record successive samples of a multivariate response the sample correlation between the columns can reveal important dependency structure in the multivariate response, e.g., stars, hubs and triangles of co-dependency. However, when the number of samples is finite and the number p of columns increases such exploration becomes futile due to a phase transition phenomenon: spurious discoveries will eventually dominate. In this presentation I will present theory for predicting these phase transitions and present Poisson limit theorems that can be used to predict finite sample behavior of correlation structure. We will discuss an application to longitudinal gene expression analysis.
Random matrices arise in many areas of engineering, social sciences, and natural sciences. For example, when rows of the random matrix record successive samples of a multivariate response the sample correlation between the columns can reveal important dependency structure in the multivariate response, e.g., stars, hubs and triangles of co-dependency. However, when the number of samples is finite and the number p of columns increases such exploration becomes futile due to a phase transition phenomenon: spurious discoveries will eventually dominate. In this presentation I will present theory for predicting these phase transitions and present Poisson limit theorems that can be used to predict finite sample behavior of correlation structure. We will discuss an application to longitudinal gene expression analysis.
Practical information
- Informed public
- Free
- This event is internal
Organizer
- EE Institute
Contact
- Philippe Gay-Balmaz, Suzanne Buffat