|Lang, S. and Fronk, Eva-Maria and Fahrmeir, Ludwig (2001): Function estimation with locally adaptive dynamic models. Collaborative Research Center 386, Discussion Paper 263|
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We present a nonparametric Bayesian method for fitting unsmooth and highly oscillating functions, which is based on a locally adaptive hierarchical extension of standard dynamic or state space models. The main idea is to introduce locally varying variances in the state equations and to add a further smoothness prior for this variance function. Estimation is fully Bayesian and carried out by recent MCMC techniques. The whole approach can be understood as an alternative to other nonparametric function estimators, such as local or penalized regression with variable bandwidth or smoothing parameter selection. Performance is illustrated with simulated data, including unsmooth examples constructed for wavelet shrinkage, and by an application to sales data. Although the approach is developed for classical Gaussian nonparametric regression, it can be extended to more complex regression problems.
|Item Type:||Paper (Research Paper)|
|Keywords:||adaptive smoothing, MCMC, nonparametric Bayesian regression, random walk priors, unsmooth functions, variable smoothing parameter|
|Collections:||Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386|
Special Research Fields > Special Research Field 386
|Subjects:||500 Science > 510 Mathematics|
|Deposited On:||05. Apr 2007|
|Last Modified:||08. Jan 2013 15:55|
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Function estimation with locally adaptive dynamic models. (deposited 04. Apr 2007)
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