Likelihood-based local linear estimation of the conditional variance function

Yu, K. and Jones, M.C. (2004). Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association, 99(465) pp. 139–144.

DOI (Digital Object Identifier) Link:	https://doi.org/10.1198/016214504000000133
Google Scholar:	Look up in Google Scholar

Abstract

We consider estimation of mean and variance functions with kernel-weighted local polynomial fitting in a heteroscedastic nonparametric regression model. Our preferred estimators are based on a localized normal likelihood, using a standard local linear form for estimating the mean and a local log-linear form for estimating the variance. It is important to allow two bandwidths in this problem, separate ones for mean and variance estimation. We provide data-based methods for choosing the bandwidths. We also consider asymptotic results, and study and use them. The methodology is compared with a popular competitor and is seen to perform better for small and moderate sample sizes in simulations. A brief example is provided.

Item Type:	Journal Item
ISSN:	1537-274X
Keywords:	bandwidth selection; heteroscedasticity; kernel estimation; nonparametric regression
Academic Unit/School:	Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID:	2130
Depositing User:	M. C. Jones
Date Deposited:	05 Jun 2006
Last Modified:	04 Oct 2016 09:46
URI:	http://oro.open.ac.uk/id/eprint/2130
Share this page:

Altmetrics

Actions (login may be required)

CORE (COnnecting REpositories)

Policies | Disclaimer