论文标题
在高维线性回归中测试异性恋性
Testing Heteroskedasticity in High-Dimensional Linear Regression
论文作者
论文摘要
我们提出了一种在高维线性回归中异质性性的新测试程序，其中协变量的数量可以大于样本量。我们的测试程序基于拉索的残差。我们证明我们的测试统计量在同性恋性的无效假设下具有渐近正态性。仿真结果表明，提出的测试程序获得了准确的经验大小和功率。我们还提出了实际经济数据应用的结果。
We propose a new testing procedure of heteroskedasticity in high-dimensional linear regression, where the number of covariates can be larger than the sample size. Our testing procedure is based on residuals of the Lasso. We demonstrate that our test statistic has asymptotic normality under the null hypothesis of homoskedasticity. Simulation results show that the proposed testing procedure obtains accurate empirical sizes and powers. We also present results of real economic data applications.
