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arXiv · 2208.12930

Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors

Mingyang Cai·Stef van Buuren·Gerko Vink
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Abstract

Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution. Many authors have studied the convergence properties of FCS when priors of conditional models are non-informative. We extend to the case of informative priors. This paper evaluates the convergence properties of the normal linear model with normal-inverse gamma prior. The theoretical and simulation results prove the convergence of FCS and show the equivalence of prior specification under the joint model and a set of conditional models when the analysis model is a linear regression with normal inverse-gamma priors.

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Mingyang Cai, Stef van Buuren, Gerko Vink. 2022-08-27. Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors. https://doi.org/10.1038/s41598-023-27786-y

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