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Likelihood-based local linear estimation of the conditional variance function.

About 16 pages (4,888 words)

Journal of the American Statistical Association, March 1st, 2004

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 po...

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Jones, M.C.; Yu, K.. Journal of the American Statistical Association, March 1st, 2004. Likelihood-based local linear estimation of the conditional variance function.. Content provided by HighBeam Research.



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