تقرير
Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors
| العنوان: | Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors |
|---|---|
| المؤلفون: | Cai, Mingyang, van Buuren, Stef, Vink, Gerko |
| سنة النشر: | 2022 |
| المجموعة: | Mathematics Statistics |
| مصطلحات موضوعية: | Statistics - Computation, Mathematics - Statistics Theory |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2208.12930 |
| رقم الأكسشن: | edsarx.2208.12930 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |