Bayesian analysis of mixture models with Yeo-Johnson transformation

Abstract

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.

KEYWORDS:

• Bayesian analysis
• Markov chain Monte Carlo methods
• mixture distributions
• Yeo-Johnson transformation

Acknowledgment

The authors thank the Editor and three reviewers for their constructive comments that have improved the initial version of this paper.

Disclosure statement

The authors report there are no competing interests to declare.

Data Availability Statement

The data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.21341088.v1