论文标题
选择模型选择后信息均匀渐近推断的混合置信区间
Hybrid Confidence Intervals for Informative Uniform Asymptotic Inference After Model Selection
论文作者
论文摘要
我提出了一种新型的置信区间，用于在使用数据选择关注模型后正确地指定任何模型，以正确地指定任何模型。该混合置信区间是通过结合选择性推理和选择后推论文献的技术来构建的，以在广泛的数据实现范围内产生短置信区间。我表明，混合置信区间具有正确的渐近覆盖范围，在不结合缩放模型参数的大量概率分布上均匀。我说明了使用LASSO目标函数选择感兴趣的回归模型，并通过一组需要各种不同数据分布的蒙特卡洛实验以及为糖尿病疾病进展的预测者提供了一些蒙特卡洛实验。
I propose a new type of confidence interval for correct asymptotic inference after using data to select a model of interest without assuming any model is correctly specified. This hybrid confidence interval is constructed by combining techniques from the selective inference and post-selection inference literatures to yield a short confidence interval across a wide range of data realizations. I show that hybrid confidence intervals have correct asymptotic coverage, uniformly over a large class of probability distributions that do not bound scaled model parameters. I illustrate the use of these confidence intervals in the problem of inference after using the LASSO objective function to select a regression model of interest and provide evidence of their desirable length and coverage properties in small samples via a set of Monte Carlo experiments that entail a variety of different data distributions as well as an empirical application to the predictors of diabetes disease progression.
