A stochastic expectation-maximization algorithm for high dimensional cognitive diagnosis models

Cognitive diagnosis models (CDMs) are psychometric tools that can provide detailed diagnostic information. The expectation-maximization (EM) algorithm is one of the most widely used methods for estimating parameters of CDMs. However, when the number of dimensions is large, the EM algorithm may become infeasible due to the computational burden. This paper proposes an efficient stochastic EM algorithm, which adopts a sequential Gibbs sampling method to reduce the computational cost. We investigate the performance of the proposed method through extensive simulations and examine its viability using two real datasets.