title: Regularized Latent Class Analysis with Application in Cognitive Diagnosis.
abstract: Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However, parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model. Our approach starts with minimal assumptions on the data structure, followed by suitable regularization to reduce complexity, so that readily interpretable, yet flexible model is obtained. An expectation-maximization-type algorithm is developed for efficient computation. It is shown that the proposed approach enjoys good theoretical properties. Results from simulation studies and a real application are presented.
authors.affiliation: Department of Psychology, Emory University, Atlanta, GA, USA.
authors.forename: Yunxiao
authors.initials: Y
authors.lastname: Chen
authors.affiliation: School of Statistics, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Xiaoou
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Statistics, Columbia University, New York, NY, USA. jcliu@stat.columbia.edu.
authors.forename: Jingchen
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Statistics, Columbia University, New York, NY, USA.
authors.forename: Zhiliang
authors.initials: Z
authors.lastname: Ying
keywords: EM algorithm; consistency; diagnostic classification models; latent class analysis; regularization