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Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density

Shang, Hanlin


Error density estimation in a nonparametric functional regression model with functional predictor and scalar response is considered. The unknown error density is approximated by a mixture of Gaussian densities with means being the individual residuals, and variance as a constant parameter. This proposed mixture error density has a form of a kernel density estimator of residuals, where the regression function is estimated by the functional Nadaraya–Watson estimator. A Bayesian bandwidth...[Show more]

CollectionsANU Research Publications
Date published: 2013-05-17
Type: Journal article
Source: Computational Statistics and Data Analysis
DOI: 10.1016/j.csda.2013.05.006


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