2023
DOI: 10.1177/1471082x231181173
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Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models

Philippe Lambert,
Oswaldo Gressani

Abstract: Laplace P-splines (LPS) combine the P-splines smoother and the Laplace approximation in a unifying framework for fast and flexible inference under the Bayesian paradigm. The Gaussian Markov random field prior assumed for penalized parameters and the Bernstein-von Mises theorem typically ensure a razor-sharp accuracy of the Laplace approximation to the posterior distribution of these quantities. This accuracy can be seriously compromised for some unpenalized parameters, especially when the information synthesiz… Show more

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Cited by 3 publications
(1 citation statement)
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“…In such scenarios, the resulting posterior distribution could exhibit skewness. Lambert and Gressani (2023) proposed an approach to address this asymmetry issue but due to complexity, this is beyond the scope of this paper.…”
Section: Laplace Approximation To the Conditional Posterior Of ξmentioning
confidence: 99%
“…In such scenarios, the resulting posterior distribution could exhibit skewness. Lambert and Gressani (2023) proposed an approach to address this asymmetry issue but due to complexity, this is beyond the scope of this paper.…”
Section: Laplace Approximation To the Conditional Posterior Of ξmentioning
confidence: 99%