A note on reference priors for the scalar skew-normal distribution

By Brunero Liseo, Nicola Maria Rinaldo Loperfido. 2006

123 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/10f80223e57db7d758572e97a7cb325838019cb5

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Citations in the corpus

Maximum penalized likelihood estimation for skew-normal and skew-t distributions (2012, methodology): “In a Bayesian framework, Liseo & Loperfido (2006) adopt the Jeffreys prior for α, which they prove to be a proper distribution over the real line.”
Skew mixture models for loss distributions: a Bayesian approach (2012, methodology): “…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 ς2 respectively, which represents a good approximation of the Jeffreys reference prior (see e.g. Bernardo, 2005) proposed by Liseo and Loperfido (2006).”
Inference in two-piece location-scale models with Jeffreys priors (2014, unknown): “…f(t) dt, α2 = 2 ∫ ∞ 0 [ 1 + t f ′(t) f(t) ]2 f(t) dt = −1 + 2 ∫ ∞ 0 t2 [ f ′(t) f(t) ]2 f(t) dt, α3 = ∫ ∞ 0 t [ f ′(t) f(t) ]2 f(t) dt. Conditions (i) and (ii) are required for the existence of the expression in (4) and are satisfied under regularity conditions (Lehmann and Casella, 1998; p. 126).”
Robust Likelihood Methods Based on the Skew‐t and Related Distributions (2008, background): “Liseo & Loperfido (2006) have shown that, in the case of the univariate SN distribution with a single shape parameter, the Jeffreys’ prior is proper.”
Analyzing skewed data by power normal model (2008, background): “Solutions to this problem have been proposed by Monti (2003), Liseo and Loperfido (2006) and Sartori (2006).”
A Framework for Skew-Probit Links in Binary Regression (2010, methodology): “Another non informative prior that has been used under the “alpha parameterization” is the Jeffreys’ prior derived in Liseo and Loperfido ( 2006 ).”