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The Family of Scale-Mixture of Skew-Normal Distributions and Its Application in Bayesian Nonlinear Regression Models
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Mahsa Abedini *   , Iraj Kazemi |
| isfahan university |
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Abstract: (5931 Views) |
| In previous studies on fitting non-linear regression models with the symmetric structure the normality is usually assumed in the analysis of data. This choice may be inappropriate when the distribution of residual terms is asymmetric. Recently, the family of scale-mixture of skew-normal distributions is the main concern of many researchers. This family includes several skewed and heavy-tailed distributions, such as the skew-t and the skew slash, as special cases and is recommended as an alternative to the normal distribution. The statistical inference based on the maximization of marginal likelihoods is complicated, in general, for non-linear regression models and thus we implement the MCMC approach to obtain Bayes estimates. Finally, we fit a non-linear regression model using proposed distributions for a real data set to show the importance of the recommended model. |
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| Keywords: Scale Mixture, Bayesian approach, Gibbs Sampler, Heavy-tailed Distributions, Metropolis-Hastings |
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Full-Text [PDF 280 kb]
(1776 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2012/11/17 | Accepted: 2015/03/1 | Published: 2015/06/17
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