Bayesian Inference for the RC(m) Association Model
研究了在双向列联表中使用RC(m)关联模型时，如何基于贝叶斯方法计算模型参数的后验分布并确定关联阶数m，通过投影方法从饱和模型先验导出简化模型先验，并用后验距离评估模型拟合。
Describing the structure in a two-way contingency table in terms of an RC(m) association model, we are concerned with the computation of posterior distributions of the model parameters using prior distributions which take into account the nonlinear restrictions of the model. We are further involved with the determination of the order of association m, based on Bayesian arguments. Using projection methods, a prior distribution over the parameters of the simpler RC(m) model is induced from a prior of the parameters of the saturated model. The fit of the assumed RC(m) model is evaluated using the posterior distribution of its distance from the full model. Our methods are illustrated with a popular dataset.