Semiparametric inference in a partial linear model
Semiparametric inference in a partial linear model
复制标题
DOI：
10.1214/aos/1034276628
复制
发表时间：
1997-02
影响因子：
4.5
通讯作者：
P. K. Bhattacharya;P. Zhao
中科院分区：
文献类型：
--
作者：
P. K. Bhattacharya;P. Zhao
在部分线性模型中，响应变量Y对协变量（W，X$）的依赖关系如下：$$Y = W \beta + \eta（X）+ \mathscr{E}$$其中$\mathscr {E}$与密度分别为g和f的$（W，X）$无关。在本文中，一个渐近有效的估计$\beta$的构造仅在温和的光滑性假设下的未知$\eta$，f和g，从而消除了假设的有限剩余方差上的所有最小二乘型估计在文献中是基于。
In a partial linear model, the dependence of a response variate Y on covariates (W, X$ is given by $$Y = W \beta + \eta(X) + \mathscr{E}$$ where $\mathscr{E}$ is independent of $(W, X)$ with densities g and f, respectively. In this paper an asymptotically efficient estimator of $\beta$ is constructed solely under mild smoothness assumptions on the unknown $\eta$, f and g, thereby removing the assumption of finite residual variance on which all least-squares-type estimators available in the literature are based.