On the role of parameterization in models with a misspecified nuisance component
Author(s)
Battey, Heather
Reid, Nancy
Type
Journal Article
Abstract
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 and easily checkable condition in terms of a notion of symmetric parameterization, leading to a 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 briefly discussed.
Date Issued
2024-09-03
Date Acceptance
2024-07-23
Citation
Proceedings of the National Academy of Sciences of USA, 2024, 121 (36)
ISSN
0027-8424
Publisher
National Academy of Sciences
Journal / Book Title
Proceedings of the National Academy of Sciences of USA
Volume
121
Issue
36
Copyright Statement
Copyright © 2024 the Author(s). Published by PNAS.
This open access article is distributed under Creative
Commons Attribution License 4.0 (CC BY).
This open access article is distributed under Creative
Commons Attribution License 4.0 (CC BY).
License URL
Identifier
https://doi.org/10.1073/pnas.2402736121
Publication Status
Published
Article Number
e2402736121
Date Publish Online
2024-08-30