Bibliographic citations
Cortés, F., (2020). Jointly modelling of cluster dependent pro les of fractional and binary variables from a Bayesian point of view [Pontificia Universidad Católica del Perú]. http://hdl.handle.net/20.500.12404/17386
Cortés, F., Jointly modelling of cluster dependent pro les of fractional and binary variables from a Bayesian point of view []. PE: Pontificia Universidad Católica del Perú; 2020. http://hdl.handle.net/20.500.12404/17386
@mastersthesis{sunedu/2655883,
title = "Jointly modelling of cluster dependent pro les of fractional and binary variables from a Bayesian point of view",
author = "Cortés Tejada, Fernando Javier",
publisher = "Pontificia Universidad Católica del Perú",
year = "2020"
}
The following thesis proposes classi cation models that consist of jointly tting longitudinal pro les of mixed fractional and binary variables modelled by zero-one beta in ated mixed regressions with cluster formation. The distinct proposed parametrizations allow di erent effects to be modelled, such as modelling the marginal mean directly through independent variables and easily interpret its e ect on it or modelling the conditional mean and the in- ation probabilities separately. In addition, individuals with similar fractional longitudinal pro les are grouped into a cluster through a latent variable, assuming that the response variables follow a nite mixture model. Due to the complexity of the models, the parameters are estimated from a Bayesian point of view by simulating a MCMC using JAGS software in R. The proposed models are tted in various simulated datasets and are compared against other models to measure performance in tting fractional longitudinal pro les and binary variables. Finally, an application on real data is conducted, consisting on longitudinal information of credit card utilization ratio and default status as dependants variables and covariates corresponding to client information, aiming to obtain clusters of clients with similar behaviour in evolution of credit card utilization and relate them to their probability of default.
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