The paper is concerned with inference for a parameter of interest in models that share a common interpretation for that parameter, but that may differ appreciably in other respects. We study the general structure of models under which the maximum likelihood estimator of the parameter of interest is consistent under arbitrary misspecification of the nuisance part of the model. A specialization of the general results to matched-comparison and two-groups problems gives a more explicit condition in terms of a new notion of symmetric parametrization, leading to an appreciable broadening and unification of existing results in those problems. The role of a generalized definition of parameter orthogonality is highlighted, as well as connections to Neyman orthogonality. The issues involved in obtaining inferential guarantees beyond consistency are discussed.

翻译：本文关注于那些对感兴趣参数具有共同解释但在其他方面可能显著不同的模型中的参数推断。我们研究了模型的一般结构，在该结构下，感兴趣参数的最大似然估计量在模型干扰部分任意误设的情况下仍具有一致性。将一般结果特化到配对比较和两组问题中，通过引入对称参数化这一新概念，得到了更显式的条件，从而显著拓宽并统一了这些问题的现有结果。文中强调了参数正交性广义定义的作用，以及其与内曼正交性的联系。此外，还讨论了超越一致性的推断保证所涉及的问题。