  Heterogeneous but complementary sources of data provide an unprecedented
opportunity for developing accurate statistical models of systems. Although the
existing methods have shown promising results, they are mostly applicable to
situations where the system output is measured in its complete form. In
reality, however, it may not be feasible to obtain the complete output
measurement of a system, which results in observations that contain missing
values. This paper introduces a general framework that integrates tensor
regression with tensor completion and proposes an efficient optimization
framework that alternates between two steps for parameter estimation. Through
multiple simulations and a case study, we evaluate the performance of the
proposed method. The results indicate the superiority of the proposed method in
comparison to a benchmark.
