Abstract
In a framework of likelihood regression model, the estimator of the response function is constructed from a set of functional units. The parameters defining these functional units are estimated with the help of Bayesian approach. The sample from the Bayes posterior distribution is obtained from the MCMC procedure based on combination of Gibbs and Metropolis-Hastings algorithms. The method is described for the case of logistic regression model and for histogram and radial basis function estimators of response function.
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© 1996 Physica-Verlag Heidelberg
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Linka, A., Picek, J., Volf, P. (1996). Bayesian Analysis for Likelihood-Based Nonparametric Regression. In: Prat, A. (eds) COMPSTAT. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-46992-3_44
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DOI: https://doi.org/10.1007/978-3-642-46992-3_44
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-0953-4
Online ISBN: 978-3-642-46992-3
eBook Packages: Springer Book Archive