论文标题
高维线性测量误差模型的推断
Inference in High-Dimensional Linear Measurement Error Models
论文作者
论文摘要
对于具有有限数量用错误测量的协变量的高维线性模型，我们研究了与容易出现错误的协变量相关的参数的统计推断，并提出了一个新的校正后校正后的反向分数测试和相应的一步估计器。我们进一步建立了新提出的测试统计量和一步估计器的渐近特性。在本地替代方案下，我们表明我们校正后的非相关得分测试统计量的限制分布是非中心正常的。通过模拟研究检查了所提出的推理程序的有限样本性能。我们通过对真实数据示例的经验分析进一步说明了提出的程序。
For a high-dimensional linear model with a finite number of covariates measured with error, we study statistical inference on the parameters associated with the error-prone covariates, and propose a new corrected decorrelated score test and the corresponding one-step estimator. We further establish asymptotic properties of the newly proposed test statistic and the one-step estimator. Under local alternatives, we show that the limiting distribution of our corrected decorrelated score test statistic is non-central normal. The finite-sample performance of the proposed inference procedure is examined through simulation studies. We further illustrate the proposed procedure via an empirical analysis of a real data example.
