论文标题
超高维线性模型的基于得分函数的测试
Score function-based tests for ultrahigh-dimensional linear models
论文作者
论文摘要
在本文中，我们研究了基于得分函数的测试，以检查模型系数的超高维副矢量的重要性，而滋扰参数矢量也是线性模型中的超高维时。我们首先重新分析并扩展了最近提出的基于得分函数的测试，以在较弱的条件下得出其限制分布在零和局部替代假设下。当测试协变量与滋扰协变量之间的相关性很高时，我们提出了一个基于两种优点的正交分数函数测试：使非分级误差项归化并降低渐近方差以增强功率性能。模拟评估了提出的测试的有限样本性能，实际数据分析说明了其应用。
In this paper, we investigate score function-based tests to check the significance of an ultrahigh-dimensional sub-vector of the model coefficients when the nuisance parameter vector is also ultrahigh-dimensional in linear models. We first reanalyze and extend a recently proposed score function-based test to derive, under weaker conditions, its limiting distributions under the null and local alternative hypotheses. As it may fail to work when the correlation between testing covariates and nuisance covariates is high, we propose an orthogonalized score function-based test with two merits: debiasing to make the non-degenerate error term degenerate and reducing the asymptotic variance to enhance power performance. Simulations evaluate the finite-sample performances of the proposed tests, and a real data analysis illustrates its application.
