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In practice, the designed nominal model set for a jump (Markov) linear system might be uncertain: 1) Every candidate model might be inexact due to, e.g., mismatched modeling assumptions or model's identification errors; 2) The nominal model set might be incomplete (e.g., the true system has three operating modes but the designed model set includes only two of them). Moreover, the designed model transition probability matrix (TPM) might be uncertain: There is a discrepancy between the designed and true TPMs. Neglecting such model uncertainties and employing nominally optimal multi-model state estimators may cause significant performance losses. Therefore, this article proposes a robust state estimation framework for jump linear systems that is insensitive to these three types of model uncertainty, technically by leveraging the distributionally robust optimization theory. Specifically, the model uncertainties …