Yu, K. and Jones, M.C.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1198/016214504000000133|
|Google Scholar:||Look up in Google Scholar|
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 Article|
|Keywords:||bandwidth selection; heteroscedasticity; kernel estimation; nonparametric regression|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||M. C. Jones|
|Date Deposited:||05 Jun 2006|
|Last Modified:||04 Oct 2016 09:46|
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