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A Formulation of the Latent Class Vector Model for Pairwise Data

Authors: Tomoya Okubo, Kuninori Nakamura, Shin-ichi Mayekawa


In this research, a latent class vector model for pairwise data is formulated. As compared to the basic vector model, this model yields consistent estimates of the parameters since the number of parameters to be estimated does not increase with the number of subjects. The result of the analysis reveals that the model was stable and could classify each subject to the latent classes representing the typical scales used by these subjects.

Keywords: finite mixture models, latent class analysis, Thrustone's paired comparison method, vector model

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