论文标题
Martingale回归模型的选择性置信区间
Selective Confidence Intervals for Martingale Regression Model
论文作者
论文摘要
在本文中，我们考虑了在可变选择后构建置信区间（特别是时间序列模型）的置信区间的问题。尽管在统计分析中构建置信区间是常见的实践，但由于所选模型的数据依赖性以及所选和未选择的变量之间的相关性，因此在我们的框架中具有挑战性。我们首先介绍所选系数的估计量，并表明在Martingale回归模型下它是一致的，其中观察结果可以取决于且误差可能是异性恋。然后，我们将估计量与重采样方法一起构建置信区间。我们的仿真结果表明，我们的方法在各种数据结构中的表现都优于其他现有方法。
In this paper we consider the problem of constructing confidence intervals for coefficients of martingale regression models (in particular, time series models) after variable selection. Although constructing confidence intervals are common practice in statistical analysis, it is challenging in our framework due to the data-dependence of the selected model and the correlation among the variables being selected and not selected. We first introduce estimators for the selected coefficients and show that it is consistent under martingale regression model, in which the observations can be dependent and the errors can be heteroskedastic. Then we use the estimators together with a resampling approach to construct confidence intervals. Our simulation results show that our approach outperforms other existing approaches in various data structures.
