Bayesian curve‐fitting with free‐knot splines

We describe a Bayesian method, for fitting curves to data drawn from an exponential family, that uses splines for which the number and locations of knots are free parameters. The method uses reversible‐jump Markov chain Monte Carlo to change the knot configurations and a locality heuristic to speed...

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Veröffentlicht in:	Biometrika 2001-12, Vol.88 (4), p.1055-1071
Hauptverfasser:	Dimatteo, Ilaria, Genovese, Christopher R., Kass, Robert E.
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation BIC Data smoothing Exact sciences and technology Generalised linear model Heuristics Knots Magnetic resonance Magnetic resonance imaging Markov analysis Markov chains Mathematical foundations Mathematical functions Mathematics Modeling Monte Carlo simulation Nonparametric regression Parametric inference Parametric models Probability and statistics Reversible‐jump Markov chain Monte Carlo Sciences and techniques of general use Signal noise Smoothing Statistics Unit‐information prior
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