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Yu, K. and Jones, M.C.
(2004).
DOI: https://doi.org/10.1198/016214504000000133
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.
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About
- Item ORO ID
- 2130
- Item Type
- Journal Item
- ISSN
- 1537-274X
- Keywords
- bandwidth selection; heteroscedasticity; kernel estimation; nonparametric regression
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Depositing User
- M. C. Jones