• Stat Methods Med Res · Oct 2016

    Bayesian analysis of transformation latent variable models with multivariate censored data.

    • Xin-Yuan Song, Deng Pan, Peng-Fei Liu, and Jing-Heng Cai.
    • Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
    • Stat Methods Med Res. 2016 Oct 1; 25 (5): 2337-2358.

    AbstractTransformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.© The Author(s) 2014.

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