Logo Logo
Switch Language to German

Biller, Clemens and Fahrmeir, Ludwig (2000): Bayesian Varying-coefficient Models using Adaptive Regression Splines. Collaborative Research Center 386, Discussion Paper 206 [PDF, 433kB]

There is a more recent version of this item available.


Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regression analysis. We present a fully Bayesian B-spline basis function approach with adaptive knot selection. For each of the unknown regression functions or varying coefficients, the number and location of knots and the B-spline coefficients are estimated simultaneously using reversible jump Markov chain Monte Carlo sampling. The overall procedure can therefore be viewed as a kind of Bayesian model averaging. Although Gaussian responses are covered by the general framework, the method is particularly useful for fundamentally non-Gaussian responses, where less alternatives are available. We illustrate the approach with a thorough application to two data sets analyzed previously in the literature: the kyphosis data set with a binary response and survival data from the Veteran's Administration lung cancer trial.

Available Versions of this Item

Actions (login required)

View Item View Item