Bayesian inference for shape mixtures of skewed distributions, with application to regression analysis
By Reinaldo Boris Arellano-Valle, Luis Mauricio Castro, Marc G. Genton, Hector Wladimir Gomez. 2008
50 citations recorded by the source corpus.
We introduce a class of shape mixtures of skewed distributions and study some of its main properties. We discuss a Bayesian interpretation and some invariance results of the proposed class. We develop a Bayesian analysis of the skew-normal, skew-generalized-normal, skew-normal-t and skew-t-normal linear re- gression models under some special prior specications for the model parameters. In particular, we show that the full posterior of the skew-normal regression model parameters is proper under an arbitrary proper prior for the shape parameter and noninformative prior for the other parameters. We implement a convenient hierar- chical representation in order to obtain the corresponding posterior analysis. We illustrate our approach with an application to a real dataset on characteristics of Australian male athletes.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7a5bf11c12f881b92cee4cff049ee95928d52e26
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In Artificial Intelligence
In Statistics and Probability
Citations in the corpus
- A Skew-Normal Mixture Regression Model (2014, methodology): “This idea has been used in conventional statistical techniques to accommodate slight deviation from normality, such as linear regression model (Arellano-Valle et al., 2008; Chen & Chen, 2003; Sahu, Dey, & Branco, 2003), nonlinear regression models (Cancho, Dey, Lachos, & Andrade, 2010; Cancho,…”
- Robust mixture regression modeling based on scale mixtures of skew-normal distributions (2015, methodology): “, 1992; Quandt & Ramsey, 1978), economics (Cosslett & Lee, 1985; Hamilton, 1989), agriculture (Turner, 2000), nutrition (Arellano-Valle et al., 2008), and psychometrics (Liu et al., 2011). The standard algorithm in this case is the so-called EM (Expectation-Maximization) of Dempster et al. (1977), o”