Bayesian fractional polynomials |
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Authors: | Daniel Sabanés Bové Leonhard Held |
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Institution: | (1) University Medical Center Freiburg, Freiburg, Germany;(2) University College London, London, UK |
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Abstract: | This paper sets out to implement the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed
error terms. Fractional polynomials widen the class of ordinary polynomials and offer an additive and transportable modelling
approach. The methodology is based on a Bayesian linear model with a quasi-default hyper-g prior and combines variable selection with parametric modelling of additive effects. A Markov chain Monte Carlo algorithm
for the exploration of the model space is presented. This theoretically well-founded stochastic search constitutes a substantial
improvement to ad hoc stepwise procedures for the fitting of fractional polynomial models. The method is applied to a data set on the relationship
between ozone levels and meteorological parameters, previously analysed in the literature. |
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