Minimizing sensitivity to model misspecification
We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, bas...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Wiley
2022
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