Variance function partially linear single-index models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 171-194 |
Journal / Publication | Journal of the Royal Statistical Society. Series B: Statistical Methodology |
Volume | 77 |
Issue number | 1 |
Online published | 8 Apr 2014 |
Publication status | Published - Jan 2015 |
Externally published | Yes |
Link(s)
Abstract
We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model. We do not insist that the variance function depends only on the mean function, as happens in the classical generalized partially linear single-index model. We develop efficient and practical estimation methods for the variance function and for the mean function. Asymptotic theory for the parametric and non-parametric parts of the model is developed. Simulations illustrate the results. An empirical example involving ozone levels is used to illustrate the results further and is shown to be a case where the variance function does not depend on the mean function.
Research Area(s)
- Asymptotic theory, Estimating equation, Identifiability, Kernel regression, Modelling ozone levels, Partially linear single-index model, Semiparametric efficiency, Single-index model, Variance function estimation
Citation Format(s)
Variance function partially linear single-index models. / Lian, Heng; Liang, Hua; Carroll, Raymond J.
In: Journal of the Royal Statistical Society. Series B: Statistical Methodology, Vol. 77, No. 1, 01.2015, p. 171-194.
In: Journal of the Royal Statistical Society. Series B: Statistical Methodology, Vol. 77, No. 1, 01.2015, p. 171-194.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review