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Title: Dimension-wise likelihood estimation of latent vector autoregressive models Authors:  Silvia Bianconcini - University of Bologna (Italy) [presenting]
Silvia Cagnone - University of Bologna (Italy)
Abstract: Approximate methods are considered for likelihood inference to longitudinal and multidimensional data within the context of health science studies. The complexity of these data necessitates the use of sophisticated statistical models, that can pose significant challenges for model fitting in terms of computational speed, memory storage, and accuracy of the estimates. The methodology is motivated by a study that examines the temporal evolution of the mental status of the US elderly population between 2006 and 2010. We propose modeling the individual mental status as a latent process also accounting for the effects of individual specific characteristics, such as gender, age, and years of educational attainment. We describe the specification of a latent vector autoregressive model within the generalized linear latent variable framework, and its efficient estimation using a recent technique, called dimension-wise quadrature.