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Likelihood-based local linear estimation of the conditional variance function

delete2004-03-01
delete61
PRE
AI
K
Keming Yu *
M
M. C. Jones
DOI：10.1198/016214504000000133delete
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Abstract

Abstract

En 中文
We consider estimation of mean and variance functions with kernel-weighted local polynomial fitting in a heteroscedastic nonparametric regression model. Our preferred estimators Lire 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.
Keywords:
bandwidth selection
heteroscedasticity
Kernel estimation
nonparametric regression
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Journal

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Journal of the American Statistical Association
IF:
3
Papers:
5.1K
Citations:
4.8W

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