title: Latent Variable Selection for Multidimensional Item Response Theory Models via [Formula: see text] Regularization.
abstract: We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an [Formula: see text] penalty term to the log-likelihood. The computation is carried out by the expectation-maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures. The method is applied to a real dataset involving the Eysenck Personality Questionnaire.
authors.affiliation: Beijing Forestry University, Beijing, China.
authors.forename: Jianan
authors.initials: J
authors.lastname: Sun
authors.affiliation: Emory University, Atlanta, USA.
authors.forename: Yunxiao
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Columbia University, New York, USA. jcliu@stat.columbia.edu.
authors.forename: Jingchen
authors.initials: J
authors.lastname: Liu
authors.affiliation: Columbia University, New York, USA.
authors.forename: Zhiliang
authors.initials: Z
authors.lastname: Ying
authors.affiliation: Beijing Normal University, Beijing, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Xin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008962:Models, Theoretical; D010556:Personality Tests; D011594:Psychometrics / Q000379:methods*; D011795:Surveys and Questionnaires
keywords: BIC; expectation–maximization; latent variable selection; multidimensional item response theory model; regularization