Likelihood-based local linear estimation of the conditional variance function

Keming Yu

doi:10.1198/016214504000000133

Likelihood-based local linear estimation of the conditional variance function

2004-03-01

61

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AI

DOI：10.1198/016214504000000133

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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.

Keyword:

bandwidth selection

heteroscedasticity

Kernel estimation

nonparametric regression

Likelihood-based local linear estimation of the conditional variance function

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