A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression
Joint Authors
Tong, Tiejun
Zhou, Yuejin
Cheng, Yebin
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-03
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Interest in variance estimation in nonparametric regression has grown greatly in the past several decades.
Among the existing methods, the least squares estimator in Tong and Wang (2005) is shown to have nice statistical properties and is also easy to implement.
Nevertheless, their method only applies to regression models with homoscedastic errors.
In this paper, we propose two least squares estimators for the error variance in heteroscedastic nonparametric regression: the intercept estimator and the slope estimator.
Both estimators are shown to be consistent and their asymptotic properties are investigated.
Finally, we demonstrate through simulation studies that the proposed estimators perform better than the existing competitor in various settings.
American Psychological Association (APA)
Zhou, Yuejin& Cheng, Yebin& Tong, Tiejun. 2014. A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-482835
Modern Language Association (MLA)
Zhou, Yuejin…[et al.]. A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression. Journal of Applied Mathematics No. 2014 (2014), pp.1-14.
https://search.emarefa.net/detail/BIM-482835
American Medical Association (AMA)
Zhou, Yuejin& Cheng, Yebin& Tong, Tiejun. A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-482835
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-482835