A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression
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 appli...
Main Authors: | Yuejin Zhou, Yebin Cheng, Tiejun Tong |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2014-01-01
|
Series: | Journal of Applied Mathematics |
Online Access: | http://dx.doi.org/10.1155/2014/585146 |
Similar Items
-
A Nonparametric Lack-of-Fit Test of Constant Regression in the Presence of Heteroscedastic Variances
by: Mohammed M. Gharaibeh, et al.
Published: (2021-07-01) -
Nonparametric lack-of-fit tests in presence of heteroscedastic variances
by: Gharaibeh, Mohammed Mahmoud
Published: (2014) -
Twin Least Squares Support Vector Regression of Heteroscedastic Gaussian Noise Model
by: Shiguang Zhang, et al.
Published: (2020-01-01) -
HATLINK: a link between least squares regression and nonparametric curve estimation
by: Einsporn, Richard L.
Published: (2017) -
Testing Heteroscedasticity in Nonparametric Regression Based on Trend Analysis
by: Si-Lian Shen, et al.
Published: (2014-01-01)