Joint distribution properties of fully conditional specification under the normal linear model with normal inverse-gamma priors

Joint distribution properties of fully conditional specification under the normal linear model with normal inverse-gamma priors

Cai, M.
van Buuren, S.
Vink, G.

2023

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 priors. 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.

Applied mathematics
Statistics
Models

http://resolver.tudelft.nl/uuid:e0ba2b6d-ebcf-442c-bbf7-d88a4204ae99

https://doi.org/10.1038/s41598-022-26933-1

981561

Scientific Reports, 13 (13)

article