Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich‐type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross‐product information matrix, the sandwich‐type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q‐matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich‐type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich‐type covariance matrix exhibits robust performance.

中文翻译：

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认知诊断模型的信息矩阵估计程序。

介绍了两种用于估计认知诊断模型（CDM）的边际最大似然估计的渐近协方差矩阵的新方法，即观测信息矩阵的逆矩阵和三明治型估计器。与以前的几个协方差矩阵估计器不同，新方法同时考虑了项和结构参数。所观察到的信息矩阵，实证叉积信息矩阵，夹心型协方差矩阵，以及两者之间的关系办法提议拉托雷（2009，J。EDUC。Behav。统计。，34进行了讨论，115）。仿真结果表明，对于正确指定的CDM和Q矩阵或使用稍微错误指定的概率模型，观察到的信息矩阵和三明治式协方差矩阵在提供项参数估计的一致标准误差方面表现出良好的性能。但是，由于模型规格不正确，只有三明治型协方差矩阵表现出强大的性能。