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Semiparametric transformation models with Bayesian P-splines

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Abstract

In this paper, we aim to develop a semiparametric transformation model. Nonparametric transformation functions are modeled with Bayesian P-splines. The transformed variables can be fitted to a general nonlinear mixed model, including linear or nonlinear regression models, mixed effect models, factor analysis models, and other latent variable models as special cases. Markov chain Monte Carlo algorithms are implemented to estimate transformation functions and unknown quantities in the model. The performance of the developed methodology is demonstrated with a simulation study. Its application to a real study on polydrug use is presented.

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  1. Department of Statistics, Chinese University of Hong Kong, Shatin, NT, Hong Kong

    Xin-Yuan Song & Zhao-Hua Lu

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  1. Xin-Yuan Song
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  2. Zhao-Hua Lu
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Correspondence to Xin-Yuan Song.

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Song, XY., Lu, ZH. Semiparametric transformation models with Bayesian P-splines. Stat Comput 22, 1085–1098 (2012). https://doi.org/10.1007/s11222-011-9280-x

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