Correspondence identifies relationships among objects via similarities among their components; it is ubiquitous in the analysis of spatial datasets, including images, weather maps, and computational simulations. This paper develops a novel multi-level mechanism for qualitative analysis of correspondence. Operators leverage domain knowledge to establish correspondence, evaluate implications for ...

Agent-based models of financial markets usually make assumptions about agent’s preferred stylized strategies. Empirical validations of these assumptions have not been performed so far on a full-market scale. Here we present a comprehensive study of the resulting strategies followed by the firms which are members of the Spanish Stock Exchange. We are able to show that they can be characterized b...

This paper proposes the first model-based clustering algorithm for multivariate functional data. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, based on the assumption of normality of the principal components, is defined and estimated by an EM-like algorithm. The main advantage of the proposed model is its ability to take into accoun...

Nonparametric approaches have recently emerged as a flexible way to model longitudinal data. This entry reviews some of the common nonparametric approaches to incorporate time and other covariate effects for longitudinally observed response data. Smoothing procedures are invoked to estimate the associated nonparametric functions, but the choice of smoothers can vary and is often subjective. Bot...

Graphical models are often used “inappropriately,” with approximations in the topology, inference, and prediction. Yet it is still common to train their parameters to approximately maximize training likelihood. We argue that instead, one should seek the parameters that minimize the empirical risk of the entire imperfect system. We show how to locally optimize this risk using back-propagation an...

Data Envelopment Analysis (DEA) models with interval inputs and outputs have been rarely discussed in DEA literature. This paper, using the enhanced Russell measurement proposes an extended model which permits the presence of interval scale variables which can take both negative and positive values. The model is compared with most well-known DEA models of which include the CCR model, the BCC mo...

Precise knowledge ofthe amount of global solar radiation plays an important role in designing solar energy systems. In this study, by using 22-year meteorologicaldata, 19 empirical models were tested for prediction of the monthly mean daily global solar radiation in Tabriz. In addition, various Artificial Neural Network (ANN) models were designed for comparison with empirical models. For this p...