A Non-iterative Bayesian Sampling Algorithm for Linear Regression Models with Scale Mixtures of Normal Distributions

Fengkai Yang () and Haijing Yuan
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Fengkai Yang: Shandong University
Haijing Yuan: Shandong University

Computational Economics, 2017, vol. 49, issue 4, No 3, 579-597

Abstract: Abstract The scale mixtures of Normal distributions are used as a robust alternative to the normal distribution in linear regression modelling, and a non-iterative Bayesian sampling algorithm is developed to obtain independently and identically distributed samples approximately from the observed posterior distributions, which eliminates the convergence problems in iterative Gibbs sampling. Model selection and influential analysis are conducted to choose the best fitted model and to detect the latent outliers. The performances of the methodologies are illustrated through several simulation studies by comparison with the Normal regression and Gibbs sampling, and finally, the US treasury bond prices data is analyzed using the proposed algorithm.

Keywords: Scale mixtures of normal distributions; EM algorithm; Inverse Bayesian formulae; Model selection; Gibbs sampling (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10614-016-9580-5

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