A generalized mixture model applied to diabetes incidence data. Issue 4 (21st March 2017)

• Record Type: Journal Article
• Authors: Zuanetti, Daiane Aparecida; Milan, Luis Aparecido
• Is Part Of: Biometrical journal. Volume 59:Issue 4(2017:Jul.)
• Page Start: 826
• Page End: 842
• Publication Date: 2017-03-21
• Subjects: Data‐driven reversible jump -- Diabetes incidence -- Longitudinal data -- Mixture models; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201600086
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract : 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.