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http://dx.doi.org/10.5351/KJAS.2018.31.6.721

Variable selection for latent class analysis using clustering efficiency  

Kim, Seongkyung (Begas)
Seo, Byungtae (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.6, 2018 , pp. 721-732 More about this Journal
Abstract
Latent class analysis (LCA) is an important tool to explore unseen latent groups in multivariate categorical data. In practice, it is important to select a suitable set of variables because the inclusion of too many variables in the model makes the model complicated and reduces the accuracy of the parameter estimates. Dean and Raftery (Annals of the Institute of Statistical Mathematics, 62, 11-35, 2010) proposed a headlong search algorithm based on Bayesian information criteria values to choose meaningful variables for LCA. In this paper, we propose a new variable selection procedure for LCA by utilizing posterior probabilities obtained from each fitted model. We propose a new statistic to measure the adequacy of LCA and develop a variable selection procedure. The effectiveness of the proposed method is also presented through some numerical studies.
Keywords
latent class analysis; clustering; variable selection; multivariate categorical data;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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