Penalized complexity priors for degrees of freedom in Bayesian P-splines

  title={Penalized complexity priors for degrees of freedom in Bayesian P-splines},
  author={Massimo Ventrucci and H{\aa}vard Rue},
  journal={Statistical Modelling},
  pages={429 - 453}
Abstract Bayesian penalized splines (P-splines) assume an intrinsic Gaussian Markov random field prior on the spline coefficients, conditional on a precision hyper-parameter τ . Prior elicitation of τ is difficult. To overcome this issue, we aim to building priors on an interpretable property of the model, indicating the complexity of the smooth function to be estimated. Following this idea, we propose penalized complexity (PC) priors for the number of effective degrees of freedom. We present… 

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