Zuanetti Daiane Aparecida, Milan Luis Aparecido
Departamento de Estatística, Universidade Federal de Sao Carlos, Rod. Washington Luís, Km 235, SP 310 Sao Carlos, São Paulo, 13565-905, Brazil.
Biom J. 2017 Jul;59(4):826-842. doi: 10.1002/bimj.201600086. Epub 2017 Mar 21.
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.
我们提出了一种对常见（独立）混合模型的推广，以适应马尔可夫一阶混合分布。我们提出了数据驱动的可逆跳跃，这是一种马尔可夫链蒙特卡罗（MCMC）程序，用于在模型选择过程中估计每个模型的后验概率并估计相应的参数。模拟数据集显示了所提出方法在收敛性、模型选择和参数估计精度方面的出色性能。最后，我们应用所提出的方法来分析美国糖尿病发病率数据集。
