.png){kind=icon}
返回
Variance function partially linear single-index models
.png){kind=icon}
OA
AI
DOI：10.1111/rssb.12066.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Asymptotic theory
Estimating equation
Identifiability
Kernel regression
Modelling ozone levels
Partially linear single-index model
Semiparametric efficiency
Single-index model
Variance function estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
Doubly robust and efficient estimators for heteroscedastic partially linear single-index models allowing high dimensional covariates允许高维协变量的异方差部分线性单指数模型的双重鲁棒和有效估计量
OAAI
OAAI分享收藏
查看更多内容