Marginal maximum likelihood estimation of single parameter logistic based on EM algorithm

Abstract

Cluster analysis is one of the most important functions of data mining. Expectation Maximization (EM) method is an important technology based on model clustering method. The expectation maximization algorithm is analyzed in this research and applied to Adaptive Testing System, in which logistic function in item response theory serves as a model, and the combination of methods of marginal maximum likelihood estimation (MMLE) and the EM algorithm are used to estimate the difficulty parameter estimation of single-parameter logistic function. This effort achieves good results.