首页> 外文期刊>Journal of statistical computation and simulation >Adaptive-weighted estimation of semi-varying coefficient models with heteroscedastic errors
【24h】

Adaptive-weighted estimation of semi-varying coefficient models with heteroscedastic errors

机译:异源误差的自适应加权估计半不同系数模型

获取原文
获取原文并翻译 | 示例

摘要

An adaptive-weighted estimation procedure for parametric and nonparametric coefficients in semi-varying coefficient models with heteroscedastic errors is considered in this paper. Firstly, we present a consistent estimator of the variance function of the error term. In order to take the heteroscedasticity into consideration, we consider the weighted local linear smoothing technique. Asymptotic properties of the proposed estimators are established. Our theoretical results demonstrate that the adaptive-weighted estimator is more efficient than the unweighted profile least-squares estimators. The simulation results show that our adaptive-weighted estimators are more efficient, compared to profile least-squares estimators and re-weighted estimators under the finite sample size. Finally, our estimation procedure is applied to a real-world data.
机译:本文考虑了具有异源误差的半变化系数模型中的参数和非参数系数的自适应加权估计过程。 首先,我们介绍了误差项的方差函数的一致估计。 为了考虑异质塑性,我们考虑加权局部线性平滑技术。 建立了拟议估计人的渐近性质。 我们的理论结果表明,自适应加权估计器比未加权的简档最小二乘估计更有效。 与轮廓最小二乘估计器和有限样本大小下的重新加权估计相比,我们的自适应加权估计器更有效。 最后,我们的估算程序适用于真实数据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号