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Autor:	Yu, K.
Título:	Likelihood-Based Local Linear Estimation of the Conditional Variance Function
Páginas/Colación:	pp. 139 - 144
Url:	http://hermia.asa.catchword.org/vl=786668/cl=114/nw=1/rpsv/cw/asa/01621459/v99n465/s14/p139

Journal of the American Statistical Association Vol. 99, no. 465 March 2004

Información de existencia

Descriptor Temático:

ESTIMACION Y ANALISIS DE VARIANZA

Resumen

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.

UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno

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