Mavridis, Dimitris; Ntzoufras, Ioannis Stochastic search item selection for factor analytic models. (English) Zbl 1410.62096 Br. J. Math. Stat. Psychol. 67, No. 2, 284-303 (2014). Summary: In this paper we implement a Markov chain Monte Carlo algorithm based on the stochastic search variable selection method of E. I. George and R. E. McCulloch [“Variable selection via Gibbs sampling”, J. Am. Stat. Assoc. 88, No. 423, 881–889 (1993; doi:10.1080/01621459.1993.10476353)] for identifying promising subsets of manifest variables (items) for factor analysis models. The suggested algorithm is constructed by embedding in the usual factor analysis model a normal mixture prior for the model loadings with latent indicators used to identify not only which manifest variables should be included in the model but also how each manifest variable is associated with each factor. We further extend the suggested algorithm to allow for factor selection. We also develop a detailed procedure for the specification of the prior parameters values based on the practical significance of factor loadings using ideas from the original work of [loc. cit.]. A straightforward Gibbs sampler is used to simulate from the joint posterior distribution of all unknown parameters and the subset of variables with the highest posterior probability is selected. The proposed method is illustrated using real and simulated data sets. Cited in 3 Documents MSC: 62H25 Factor analysis and principal components; correspondence analysis 62D05 Sampling theory, sample surveys Keywords:Markov chain Monte Carlo; Gibbs sampling; stochastic search variable selection; factor selection; prior specification PDFBibTeX XMLCite \textit{D. Mavridis} and \textit{I. Ntzoufras}, Br. J. Math. Stat. Psychol. 67, No. 2, 284--303 (2014; Zbl 1410.62096) Full Text: DOI