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Latent class CUB models

Leonardo Grilli (), Maria Iannario (), Domenico Piccolo () and Carla Rampichini ()

Advances in Data Analysis and Classification, 2014, vol. 8, issue 1, 105-119

Abstract: The paper proposes a latent class version of Combination of Uniform and (shifted) Binomial random variables ( CUB ) models for ordinal data to account for unobserved heterogeneity. The extension, called LC-CUB , is useful when the heterogeneity is originated by clusters of respondents not identified by covariates: this may generate a multimodal response distribution, which cannot be adequately described by a standard CUB model. The LC-CUB model is a finite mixture of CUB models yielding a multimodal theoretical distribution. Model identification is achieved by constraining the uncertainty parameters to be constant across latent classes. A simulation experiment shows the performance of the maximum likelihood estimator, whereas the usefulness of the approach is illustrated by means of a case study on political self-placement measured on an ordinal scale. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Finite mixture; Maximum likelihood; Ordinal data; Simulation; Unobserved heterogeneity; 62F99; 62J99 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11634-013-0143-5

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