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

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 di...

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Bibliographic Details
Published in:Computational economics Vol. 49; no. 4; pp. 579 - 597
Main Authors: Yang, Fengkai, Yuan, Haijing
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2017
Springer Nature B.V
Subjects:
Algorithms
Bayesian analysis
Behavioral/Experimental Economics
Computer Appl. in Social and Behavioral Sciences
Convergence
Economic theory
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Grammatical aspect
Math Applications in Computer Science
Measures
Mixtures
Normal distribution
Operations Research/Decision Theory
Prices
Random variables
Regression analysis
Sampling
Simulation
Stock prices
Students
Studies
Treasury bonds
United States
Inverse Bayesian formulae
Gibbs sampling
Model selection
EM algorithm
Scale mixtures of normal distributions
ISSN:0927-7099, 1572-9974
Online Access:Get full text
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Summary: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.
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ISSN:0927-7099
1572-9974
DOI:10.1007/s10614-016-9580-5