Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

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

Date

Authors

Zhang, Xibin

King, Maxwell

Shang, Hanlin

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

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

URI

http://hdl.handle.net/1885/23388

Collections

ANU Research Publications

Source

Computational Statistics and Data Analysis

Type

Journal article

DOI

10.1016/j.csda.2014.04.016

Restricted until

2037-12-31

Downloads

File

Description

01_Zhang_A_sampling_algorithm_for_2014.pdf (693.98 KB)