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Skew‐normal linear calibration: a Bayesian perspective
Author(s) -
Figueiredo Cléber da Costa,
Sandoval Mônica Carneiro,
Bolfarine Heleno,
Lima Claudia Regina O.P.
Publication year - 2008
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1178
Subject(s) - skew , gibbs sampling , deviance (statistics) , bayesian probability , calibration , computer science , bayesian inference , deviance information criterion , econometrics , mathematics , algorithm , data mining , statistics , artificial intelligence , machine learning , telecommunications
In this paper, we present a Bayesian approach for estimation in the skew‐normal calibration model, as well as the conditional posterior distributions which are useful for implementing the Gibbs sampler. Data transformation is thus avoided by using the methodology proposed. Model fitting is implemented by proposing the asymmetric deviance information criterion, ADIC, a modification of the ordinary DIC. We also report an application of the model studied by using a real data set, related to the relationship between the resistance and the elasticity of a sample of concrete beams. Copyright © 2008 John Wiley & Sons, Ltd.