
Abstract
Common nonparametric curve fitting methods such as spline smoothing, local polynomial regression and basis function approaches are now well developed and widely applied. More recently, Bayesian function estimation has become a useful supplementary or alternative tool for practical data analysis, mainly due to breakthroughs in computerintensive inference via Markov chain Monte Carlo simulation. This paper surveys recent developments in semiparametric Bayesian inference for generalized regression and outlines some directions in current research.
Item Type: | Paper |
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Faculties: | Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386 Special Research Fields > Special Research Field 386 |
Subjects: | 500 Science > 510 Mathematics |
URN: | urn:nbn:de:bvb:19-epub-1526-7 |
Language: | English |
Item ID: | 1526 |
Date Deposited: | 04. Apr 2007 |
Last Modified: | 04. Nov 2020 12:45 |