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We propose a new modeling paradigm for large dimensional aggregates of stochastic systems by Generalized Factor Analysis (GFA) models. These models describe the data as the sum of a flocking plus an uncorrelated idiosyncratic component. The flocking component describes a sort of collective orderly motion which admits a much simpler mathematical description than the whole ensemble while the idiosyncratic...
In this paper, we study modeling and identification of stochastic systems by Generalized Factor Analysis models. Although this class of models was originally introduced for econometric purposes, we present some possible applications of engineering interest. In particular, we show that there is a natural connection between Generalized Factor Analysis models and multi-agents systems. The common factor...
An interesting generalization of dynamic factor analysis models has been proposed recently by Forni, Lippi and collaborators. These models, called generalized dynamic factor analysis models describe observations of infinite cross-sectional dimension. Quite surprisingly the inherent non-uniqueness of factor analysis models does not occur in this generalized context. We attempt an explanation of this...
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