Sampling from the posterior distribution in generalized linear mixed models
Statistics and Computing
Statistics and Computing
Enhancing scatterplots with smoothed densities
Bioinformatics
Generalized structured additive regression based on Bayesian P-splines
Computational Statistics & Data Analysis
Improved predictions penalizing both slope and curvature in additive models
Computational Statistics & Data Analysis
Editorial: Computational statistics within clinical research
Computational Statistics & Data Analysis
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A simple parametrization, built from the definition of cubic splines, is shown to facilitate the implementation and interpretation of penalized spline models, whatever configuration of knots is used. The parametrization is termed value-first derivative parametrization. Inference is Bayesian and explores the natural link between quadratic penalties and Gaussian priors. However, a full Bayesian analysis seems feasible only for some penalty functionals. Alternatives include empirical Bayes inference methods involving model selection type criteria. The proposed methodology is illustrated by an application to survival analysis where the usual Cox model is extended to allow for time-varying regression coefficients.