Cognitive diagnosis models have been popularly used in fields such as education, psychology, and social sciences. While parametric likelihood estimation is a prevailing method for fitting cognitive diagnosis models, nonparametric methodologies are attracting increasing attention due to their ease of implementation and robustness, particularly when sample sizes are relatively small. However, existing clustering consistency results of the nonparametric estimation methods often rely on certain restrictive conditions, which may not be easily satisfied in practice. In this article, the clustering consistency of the general nonparametric classification method is reestablished under weaker and more practical conditions.

翻译：认知诊断模型已在教育、心理学和社会科学等领域得到广泛应用。虽然参数似然估计是拟合认知诊断模型的常用方法，但非参数方法因其易于实现且稳定性强（尤其在样本量相对较小时）而日益受到关注。然而，现有非参数估计方法的聚类一致性结果往往依赖于某些在实际中不易满足的严格条件。本文在更弱且更具实用性的条件下重新建立了一般非参数分类方法的聚类一致性。