Penalized complexity priors for degrees of freedom in Bayesian P-splines
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Publication:5142159
DOI10.1177/1471082X16659154OpenAlexW2245477318MaRDI QIDQ5142159
Publication date: 30 December 2020
Published in: Statistical Modelling (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1511.05748
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Cites Work
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- Robust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models
- Scale-dependent priors for variance parameters in structured additive distributional regression
- Fast and compact smoothing on large multidimensional grids
- Propriety of posteriors in structured additive regression models: Theory and empirical evi\-dence
- Auxiliary mixture sampling with applications to logistic models
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- Penalising model component complexity: a principled, practical approach to constructing priors
- Bayesian and frequentist regression methods
- Stochastic model specification search for Gaussian and partial non-Gaussian state space models
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Bayesian inference for generalized linear mixed models
- Generalized Linear Array Models with Applications to Multidimensional Smoothing
- Semiparametric Regression
- On Block Updating in Markov Random Field Models for Disease Mapping
- Regression
- Gaussian Markov Random Fields
- Simultaneous probability statements for Bayesian P-splines
- On Information and Sufficiency
- Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)
