Computing conditional maximum likelihood estimates for generalized Rasch models using simple loglinear models with diagonals parameters
将Andersen提出的广义Rasch模型与拟对称对数线性模型联系起来，通过将受试者参数视为随机效应，拟合对数线性模型得到项目参数的条件最大似然估计，并推广了Tjur关于二分响应Rasch模型与对数线性模型联系的观察。
Generalized Rasch models for multiple-response items proposed by Andersen (1973) are related to quasi-symmetric loglinear models. The loglinear models are obtained by treating subject parameters in the Rasch models as random effects. Fitting the loglinear models yields estimates of item parameters in the generalized Rasch models that are also conditional maximum likelihood estimates when the subject effects are treated as fixed. For models that apply naturally when there are ordinal response categories, the related loglinear models are simple quasi-symmetric models having diagonals parameters. Our results generalize Tjur's (1982) observation about the connection between binary-response Rasch models and loglinear models.