Bayesian inference for the skewness parameter of the scalar skew-normal distribution

By C. Bayes. 2007

86 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Robust Likelihood Methods Based on the Skew‐t and Related Distributions (2008, methodology): “In that setting, Bayes & Branco (2007) have shown that the Jeffreys’ prior is well approximated by a Student’s t distribution.”
Maximum penalized likelihood estimation for skew-normal and skew-t distributions (2012, methodology): “Follow-up work has been done by Bayes & Branco (2007) whose development includes a closed-form approximation M(α) ≈ −3α 2 ( 1 + 8α2 π2 )−1 .”
Skew mixture models for loss distributions: a Bayesian approach (2012, methodology): “For the skewness parameters αl, as suggested by Bayes and Branco (2007), we consider a Student t distribution with one-half degrees of freedom, location and scale parameters equal to 0 and ς(2) respectively, which represents a good approximation of the Jeffreys reference prior (see e.”
Moderated Mediation Analysis Using Bayesian Methods (2015, background): “Researchers have found that different Bayesian estimators (e.g., posterior mean, posterior median, posterior mode) combined with different priors could have different empirical performance when inferring a parameter with a skewed distribution (e.g., Bayes & Branco, 2007; Browne & Draper, 2006).”