.png){kind=icon}
Return
Variance function partially linear single-index models
.png){kind=icon}
OA
AI
DOI：10.1111/rssb.12066.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Asymptotic theory
Estimating equation
Identifiability
Kernel regression
Modelling ozone levels
Partially linear single-index model
Semiparametric efficiency
Single-index model
Variance function estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave View more