The LiU Seminar Series in Statistics and Mathematical Statistics
The seminars are typically held bi-weekly on Tuesdays at 15.15-16.15.
For further information, or if you want to be notified about the seminars by e-mail, please contact Mattias Villani.
Thursday, May 16, 3.15 pm, 2013. Seminar in StatisticsDimension reduction: modern aspects
Nickolay Trendafilov, Department of Mathematics and Statistics, The Open University, UK
Abstract: The talk is divided into two parts: principal component analysis and exploratory factor analysis. Principal component analysis (PCA) is well known technique for dimension reduction. For PCA, the basic formulations and features of the technique will be recalled. Next, the classic ways to interpret PCA solutions will be discussed. Their limitations and shortcomings are overcome by adopting a new approach for PCA interpretation, called sparse PCA. This approach produces sparse components loading in a sense that each principal component is composed by only few original variables. This makes the interpretation easier and objective, especially when large number of variables are involved. Exploratory factor analysis (EFA) is a less popular technique for dimension reduction than PCA. It will be explained why this is the case, and why it makes sense to reconsider EFA. The EFA basic formulations and features will be explained. Next, a modern generalization of the classic EFA will be described. This new development makes it possible to analyze data with more variables than observations, which is the typical data format in modern applications as climate, gene, etc data analysis.
[ Slides ]
Location: Alan Turing
Future and Past SeminarsSpring 2013 | Fall 2012 | Spring 2012 | Fall 2011
Page responsible: Mattias Villani
Last updated: 2013-05-17