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.
CITATION STYLE
Lian, H., Liang, H., & Carroll, R. J. (2015). Variance function partially linear single-index models. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 77(1), 171–194. https://doi.org/10.1111/rssb.12066
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