Statistics Seminars: Flexible Bivariate Regression
18 January 2016 14:00 in CM221
This work is about bivariate copula-based regression models for continuous margins, binary margins and a mixture of the two. The proposed approach allows for the simultaneous estimation of the marginal distribution parameters and copula coefficient. Furthermore, each parameter of the implemented bivariate distributions can be flexibly modeled in a regression setting using different types of covariate effects (e.g., non-linear, random and spatial effects). Parameter estimation is achieved using a computationally stable and efficient algorithm based on the penalized likelihood framework. The models are implemented in the SemiParBIVProbit R package which is very easy to use. The approach will be motivated and illustrated using a study on HIV.
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