A generalized mixture model applied to diabetes incidence data.
We present a generalization of the usual (independent) mixture model to accommodate a Markovian first-order mixing distribution. We propose the data-driven reversible jump, a Markov chain Monte Carlo (MCMC) procedure, for estimating the a posteriori probability for each model in a model selection procedure and estimating the corresponding parameters. Simulated datasets show excellent performance of the proposed method in the convergence, model selection, and precision of parameters estimates. Finally, we apply the proposed method to analyze USA diabetes incidence datasets.
Departamento de Estatística, Universidade Federal de Sao Carlos, Rod. Washington Luís, Km 235, SP 310 Sao Carlos, São Paulo, 13565-905, Brazil.
Daiane Aparecida
DA
Zuanetti
Departamento de Estatística, Universidade Federal de Sao Carlos, Rod. Washington Luís, Km 235, SP 310 Sao Carlos, São Paulo, 13565-905, Brazil.
Luis Aparecido
LA
Milan
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D014481:United States / Q000453:epidemiology
Data-driven reversible jump; Diabetes incidence; Longitudinal data; Mixture models