A sampling algorithm for bandwidth estimation in an nonparametric regression model with a flexible error density

Date

2014

Authors

Zhang, Xibin
King, Maxwell
Shang, Hanlin

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Abstract

The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussian densities with means being the individual error realizations and variance a constant parameter. Such a mixture density has the form of a kernel density

Description

Keywords

Keywords: Learning algorithms; Mathematical models; Mixtures; Regression analysis; Value engineering; Bayes factor; Error density; Metropolis-Hastings algorithm; Predictive density; Value at Risk; Bandwidth Bayes factors; Kernel-form error density; Metropolis-Hastings algorithm; Posterior predictive density; State-price density; Value-at-risk

Citation

Source

Computational Statistics and Data Analysis

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1016/j.csda.2014.04.016

Restricted until

2037-12-31