Simplified Maximum Likelihood Inference Based on the Likelihood Decomposition for Missing Data

Simplified Maximum Likelihood Inference Based on the Likelihood Decomposition for Missing Data

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Article ID: iaor201524977
Volume: 55
Issue: 3
Start Page Number: 271
End Page Number: 283
Publication Date: Sep 2013
Journal: Australian & New Zealand Journal of Statistics
Authors: ,
Keywords: estimation, maximum likelihood estimation
Abstract:

In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non‐monotone missing data. Numerical examples are presented to illustrate this method.

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