Consistency Theory of General Nonparametric Classification Methods in Cognitive Diagnosis
Year: 2025
Type: article
Abstract: Cognitive diagnosis models (CDMs) have been popularly used in fields such as education, psychology, and social sciences. While parametric likelihood estimation is a prevailing method for fitting CDMs, nonparametric methodologies are attracting increasing attention due to their ease of implementation and robustness, particularly when sample sizes are relatively small. However, existing 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 consistency theory for the general nonparametric classification method is reestablished under weaker and more practical conditions.
Source: Psychometrika
DOI: 10.1017/psy.2025.9
Authors: Chengyu Cui, Yanlong Liu, Gongjun Xu
Institutions: University of Michigan, University of Chicago
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
OpenAlex ID: W4408499452