Consistency Theory of General Nonparametric Classification Methods in Cognitive Diagnosis

Abstract

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.