Research Seminar
A general framework for simultaneous component methodsKatrijn Van DeunKU Leuven | |
| Abstract: | Nowadays frequently different pieces of information are gathered on the same set of entities or variables with the different pieces stemming, for example, from different conditions or measurement techniques. This implies that more and more data appear that consist of two or more data arrays that have a shared mode. A broad range of methods can be used to analyze such data, an important class of them originating from the component analysis domain, called simultaneous component methods (e.g., SUM-PCA, unrestricted PCovR, MFA, STATIS, and SCA-P). Yet, different simultaneous component methods may lead to quite different results. Moreover, the methods are not easy to compare as they stem from different research domains in which different terminologies are being used. In this paper we offer a general framework that encompasses all simultaneous component methods and that highlights both the common core of the methods and the specific elements with regard to which they differ. An overview of principles is given that may guide the data analyst in choosing an appropriate simultaneous components method. Several theoretical and practical issues are illustrated with an empirical example on metabolomics data as obtained with different separation methods. |
| Date: | Tue Nov 4, 12:15 pm - 1:15 pm |
| Place: | room 00.60 (Department of Psychology, Tiensestraat 102, 3000 Leuven) |