Title: A sparse common component approach with selection of relevant clusters of variables
Authors: Katrijn Van Deun - Tilburg University (Netherlands) [presenting]
Abstract: Social science research has entered the era of big data: Many detailed measurements are taken and multiple sources of information are used to unravel complex multivariate relations. For example, in studying obesity as the outcome of environmental and genetic influences, researchers increasingly collect survey, dietary, biomarker and genetic data from the same individuals. Although linked more-variables-than-samples (called high-dimensional) multi-source data form an extremely rich resource for research, extracting meaningful and integrated information is challenging and not appropriately addressed by current statistical methods. A first problem is that relevant information is hidden in a bulk of irrelevant variables with a high risk of finding incidental associations. Second, the sources are often very heterogeneous, which may obscure apparent links between the shared mechanisms. Hence, a statistical framework is needed to (i) select the relevant groups of variables within each source and (ii) link them throughout data sources. To address these issues, we present an extension of sparse simultaneous component analysis to a component method that 1) finds the common components and 2) that selects relevant clusters of variables.