Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866917489115398144 |
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| author | Cai, Mingyang van Buuren, Stef Vink, Gerko |
| author_facet | Cai, Mingyang van Buuren, Stef Vink, Gerko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_12930 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors Cai, Mingyang van Buuren, Stef Vink, Gerko Computation Statistics Theory 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. |
| title | Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors |
| topic | Computation Statistics Theory |
| url | https://arxiv.org/abs/2208.12930 |