In this article, we consider the semiparametric regression model: $y_i^{(n)}=x_i^{(n)}\beta +g(t_i^{(n)})+{\epsilon }_i^{(n)},i=1,2,\dots ,n,n\ge 1$, which is an important and very useful statistical model. We investigate the parametric component and nonparametric component estimators in a semiparametric regression model based on negatively associated random errors. The weak consistency for the estimators ${\hat{\beta }}_n$ and ${\hat{g}}_n(t)$ of $\beta$ and $g(t)$ respectively are established under some suitable conditions. In addition, a simulation to study the numerical performance of the consistency for the nearest neighbor weight function estimators is provided and a real data application is then presented.

在本文中，我们考虑半参数回归模型： ${是}_{\mathrm{一世}}^{(n)}=X_{\mathrm{一世}}^{(n)}\beta +G({吨}_{\mathrm{一世}}^{(n)})+{\epsilon }_{\mathrm{一世}}^{(n)},\mathrm{一世}=1,2,\dots ,n,n\ge 1$，这是一个重要且非常有用的统计模型。我们研究了基于负相关随机误差的半参数回归模型中的参数分量和非参数分量估计量。估计量的弱一致性${\hat{\beta }}_n$ 和 ${\hat{G}}_n(吨)$ 的 $\beta$ 和 $G(吨)$分别是在一些合适的条件下建立的。此外，还提供了一个模拟来研究最近邻权重函数估计器的一致性的数值性能，然后提出了一个实际的数据应用。