Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data

Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data

Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is misspecified. In this article, we create a for...

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Bibliographic Details
Main Authors: Richard M. Golden, Steven S. Henley, Halbert White, T. Michael Kashner
Format: Article
Language:English
Published: MDPI AG 2019-09-01
Series:Econometrics
Subjects:
asymptotic theory
ignorable
Generalized Information Matrix Test
misspecification
missing data
nonignorable
sandwich estimator
specification analysis
Online Access:https://www.mdpi.com/2225-1146/7/3/37

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https://www.mdpi.com/2225-1146/7/3/37

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