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We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates
Enlace original:
http://repositorio.anid.cl/handle/10533/233447
We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates
Enlace original:
http://repositorio.anid.cl/handle/10533/233446
This work takes up methods for bayesian inference in generalized linear mixed models with applications to small-area estimation. A previous work (Datta and Lahiri, 1995) focused on Bayesian estimation with a prior scale mixture distribution for the error component in a normal linear model, to smo...
Enlace original:
http://repositorio.anid.cl/handle/10533/179345
Bayesian nonparametric (BNP) statistics is a relative new area of statistics. The intersection of Bayesian and non-parametric statistics was almost empty until the sixties and seventies where the first advances were made, primarily on the mathematical for
Enlace original:
http://repositorio.anid.cl/handle/10533/235619
