具有相依观测值的广义线性模型的模型比较
Model comparison for generalized linear models with dependent observations
摘要：
给出了一类广义线性模型的边际拟似然函数的随机展开式。在此基础上，提出了一种能够处理误指定模型和相依数据的准贝叶斯信息准则，从而在理论上扩展了经典的Schwarz贝叶斯信息准则.同时证明了该准则相对于最优模型具有模型选择一致性。一些说明性的数值例子和实际数据的例子。
英文摘要：
The stochastic expansion of the marginal quasi-likelihood function associated with a class of generalized linear models is shown. Based on the expansion, a quasi-Bayesian information criterion is proposed that is able to deal with misspecified models and dependent data, resulting in a theoretical extension of the classical Schwarz's Bayesian information criterion. It is also proved that the proposed criterion has model selection consistency with respect to the optimal model. Some illustrative numerical examples and a real data example are presented.
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