Abstract
–Mixture models are widely employed in the analysis of heterogeneous data. However, existing approaches are based on the assumption that the observations in each component are normally distributed. The main objective of this article is to propose mixture models with Yeo-Johnson transformation to handle general heterogeneous data. Bayesian methods are developed for estimation and model comparison. The empirical performance of the proposed methodology is assessed through simulation studies. A real analysis of a data set derived from the National Longitudinal Survey of Youth 1997 is presented for illustration.
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