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A generalized mixture model applied to diabetes incidence data
Author(s) -
Zuanetti Daiane Aparecida,
Milan Luis Aparecido
Publication year - 2017
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201600086
Subject(s) - markov chain monte carlo , reversible jump markov chain monte carlo , model selection , generalization , computer science , convergence (economics) , mathematics , monte carlo method , selection (genetic algorithm) , mixing (physics) , algorithm , statistics , mathematical optimization , machine learning , economic growth , mathematical analysis , physics , quantum mechanics , economics
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.