Preterm births represent a serious medical issue since they can affect the health of the mother and the fetus. Our idea is to study the dependence between preterm births in repeated pregnancies using a vine copula approach. More precisely, we model marginals with generalized additive models for location scale and shape (GAMLSS) and we follow a Bayesian nonparametric approach to estimate the pair copulas in the vine. Our approach has two main advantages compared to the traditional methods: on the one hand it is extremely flexible, due to the vine structure, and on the other hand it overcomes the need of specify the families of each pair copula.
Barone, R., Dalla Valle, L., Bayesian nonparametric approach for vine copula modelling: an application to preterm birth data in repeated pregnancies, in Proocedings of the 34th International Workshop on Statistical Modeling, (Guimariies - Portugal - University of Minho, 07-12 July 2019), International Workshop on Statistical Modelling, Guimaraes 2019: 45-49 [https://hdl.handle.net/10807/323965]
Bayesian nonparametric approach for vine copula modelling: an application to preterm birth data in repeated pregnancies
Barone, Rosario
;
2019
Abstract
Preterm births represent a serious medical issue since they can affect the health of the mother and the fetus. Our idea is to study the dependence between preterm births in repeated pregnancies using a vine copula approach. More precisely, we model marginals with generalized additive models for location scale and shape (GAMLSS) and we follow a Bayesian nonparametric approach to estimate the pair copulas in the vine. Our approach has two main advantages compared to the traditional methods: on the one hand it is extremely flexible, due to the vine structure, and on the other hand it overcomes the need of specify the families of each pair copula.
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