IPAD: Stable Interpretable Forecasting with Knockoffs Inference.
IPAD: Stable Interpretable Forecasting with Knockoffs Inference.
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iPad：Stable Interpretable Forecasting with Knockoffs Inference。
DOI：
10.1080/01621459.2019.1654878
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发表时间：
2020
影响因子：
3.7
通讯作者：
Uematsu Y
中科院分区：
文献类型：
--
作者：
Fan Y;Lv J;Sharifvaghefi M;Uematsu Y
可解释性和稳定性是统计学、经济学和金融学中出现的许多当代大数据应用所期望的两个重要特征。虽然前者在某种程度上被许多现有的预测方法所享有，但后者在控制错误发现的特征的比例的意义上，可以大大提高可解释性，这在很大程度上仍然是欠发达的。为此，在本文中，我们利用了最近在Candès，Fan，Janson和Lv（2018）中引入的模型X仿制品的一般框架，这对于可再现的大规模推理来说是非常规的，因为该框架完全不使用p值进行显著性检验，提出了一种新的交织概率因子解耦（iPad）方法，用于高风险环境下具有山寨推断的稳定可解释预测。三维模型该方法的秘诀是通过假设一个在经济学和金融学中广泛使用的协变量关联结构的潜在因素模型来构造敲除变量。我们的方法和工作是不同于现有的文献中，我们估计的协变量分布的数据，而不是假设它是已知的构建敲除变量时，我们的程序不需要任何样本分裂，我们提供了理论上的理由渐近错误发现率控制，和功率分析的理论也成立。仿真实例和真实的数据分析进一步表明，与一些常用的预测方法相比，该方法具有良好的有限样本性能，并具有良好的可解释性和稳定性。
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in statistics, economics, and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped. To this end, in this paper we exploit the general framework of model-X knockoffs introduced recently in Candès, Fan, Janson and Lv (2018), which is nonconventional for reproducible large-scale inference in that the framework is completely free of the use of p-values for significance testing, and suggest a new method of intertwined probabilistic factors decoupling (IPAD) for stable interpretable forecasting with knockoffs inference in high-dimensional models. The recipe of the method is constructing the knockoff variables by assuming a latent factor model that is exploited widely in economics and finance for the association structure of covariates. Our method and work are distinct from the existing literature in that we estimate the covariate distribution from data instead of assuming that it is known when constructing the knockoff variables, our procedure does not require any sample splitting, we provide theoretical justifications on the asymptotic false discovery rate control, and the theory for the power analysis is also established. Several simulation examples and the real data analysis further demonstrate that the newly suggested method has appealing finite-sample performance with desired interpretability and stability compared to some popularly used forecasting methods.
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影响因子：
4.5
作者：
Lv, Jinchi
通讯作者：
Lv, Jinchi
DOI：
10.1111/rssb.12234
发表时间：
2018-01-01
影响因子：
5.8
作者：
Shah, Rajen D.;Buhlmann, Peter
通讯作者：
Buhlmann, Peter
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
DOI：
10.1080/01621459.2012.720478
发表时间：
2012-09-01
影响因子：
3.7
作者：
Fan, Jianqing;Han, Xu;Gu, Weijie
通讯作者：
Gu, Weijie
影响因子：
4.5
作者：
Fan, Jianqing;Fan, Yingying
通讯作者：
Fan, Yingying