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: 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