Missing at random, likelihood ignorability and model completeness

By Guobing Lu, J. Copas. 2004

52 citations recorded by the source corpus.

This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a model for the missing data mechanism (MDM). If the response model is given by a complete density family, then frequentist inference from the likelihood function ignoring the MDM is valid if and only if the MDM is MAR. This necessary and sufficient condition also holds more generally for models for coarse data, such as censoring. Examples are given to show the necessity of the completeness of the underlying model for this equivalence to hold.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f7be5d3ba162afdcbacc93aee9bff72422d774f8

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Local model uncertainty and incomplete‐data bias (with discussion) (2005, background): “Under data MAR, the missing data mechanism is not allowed to depend on the values of any unobserved components of t. Lu and Copas (2004) showed that if fT is a complete distribution family then data MAR is a necessary and sufficient condition for the missing data process to be ignorable, in the…”
Estimating Equations Inference With Missing Data (2008, background): “Recent studies include Aerts et al. (2002), Wang and Rao (2002), Chen, Ibrahim, and Shao (2004), Qi, Wang, and Prentice (2005), Lu and Copas (2005), among others.”