A Bayesian hierarchical sparse factor model for estimating simultaneous covariance matrices for gestational outcomes in consecutive pregnancies.
Stat Med
; 42(19): 3353-3370, 2023 08 30.
Article
em En
| MEDLINE
| ID: mdl-37276864
ABSTRACT
Covariance estimation for multiple groups is a key feature for drawing inference from a heterogeneous population. One should seek to share information about common features in the dependence structures across the various groups. In this paper, we introduce a novel approach for estimating the covariance matrices for multiple groups using a hierarchical latent factor model that shrinks the factor loadings across groups toward a global value. Using a sparse spike and slab model on these loading coefficients allows for a sparse formulation of our model. Parameter estimation is accomplished through a Markov chain Monte Carlo scheme, and a model selection approach is used to select the number of factors to use. We validate our model through extensive simulation studies. Finally, we apply our methodology to the NICHD Consecutive Pregnancies Study to estimate the correlations between birth weights and gestational ages of three consecutive birth within four different subgroups (underweight, normal, overweight, and obese) of women.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Limite:
Female
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Humans
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Pregnancy
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article