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Heterogeneous but complementary sources of data provide an unprecedentedopportunity for developing accurate statistical models of systems. Although theexisting methods have shown promising results, they are mostly applicable tosituations where the system output is measured in its complete form. Inreality, however, it may not be feasible to obtain the complete outputmeasurement of a system, which results in observations that contain missingvalues. This paper introduces a general framework that integrates tensorregression with tensor completion and proposes an efficient optimizationframework that alternates between two steps for parameter estimation. Throughmultiple simulations and a case study, we evaluate the performance of theproposed method. The results indicate the superiority of the proposed method incomparison to a benchmark.

An Augmented Regression Model for Tensors with Missing Values