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B1923
Title: Finding the hidden link: Sparse common component analysis Authors:  Katrijn Van Deun - Tilburg University (Netherlands) [presenting]
Abstract: Recent technological advances have made it possible to study human behavior by linking novel types of data to more traditional types of psychological data, for example linking psychological questionnaire data with genetic risk scores. Revealing the variables that are linked throughout these traditional and novel types of data gives crucial insight in the complex interplay between the multiple factors that determine human behavior, e.g., the concerted action of genes and environment in the emergence of depression. Little or no theory is available on the link between such traditional and novel types of data, the latter usually consisting of a huge number of variables. The challenge is to select - in an automated way - those variables that are linked throughout the different blocks and this eludes current available methods for data analysis. To fill the methodological gap, we present an extension of simultaneous component analysis. Constraints are introduced to impose block-structure and to force automated selection of the relevant variables. We will present an efficient procedure that is scalable to the setting of a very large number of variables. Using simulated data and an empirical example, we will showcase the benefits of the proposed method and compare with various competing methods, including sparse PCA and rotation techniques.