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A semiparametric Bayesian approach to joint mean and variance models. (English) Zbl 1281.62074
Summary: We propose a fully Bayesian inference for semiparametric joint mean and variance models on the basis of B-spline approximations of nonparametric components. An efficient MCMC method which combines the Gibbs sampler and Metropolis-Hastings algorithm is suggested for the inference, and the methodology is illustrated through a simulation study and a real example.

MSC:
62F15 Bayesian inference
62G08 Nonparametric regression and quantile regression
65C40 Numerical analysis or methods applied to Markov chains
65C60 Computational problems in statistics (MSC2010)
Software:
GLIM; SemiPar
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