This package aims to estimate bandwidths in the regression function and kernel-form error density simultaneously, using a Bayesian sampling algorithm. We demonstrate this Bayesian sampling algorithm using a functional nonparametric regression and a semi-functional partial linear model
The regression function is approximated by the functional Nadaraya-Watson estimator, while the unknown error density is approximated by a kernel density of residuals. In both regression function and error density, they depend crucially on the selection of the optimal bandwidths.
Han Lin Shang
Maintainer: Han Lin Shang <H.Shang@soton.ac.uk>
H. L. Shang (2013) Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density, Computational Statistics, in press.
H. L. Shang (2013) Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density, Computational Statistics and Data Analysis, 67, 185-198.
X. Zhang and R. D. Brooks and M. L. King (2009) A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation, Journal of Econometrics, 153, 21-32.
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