摘要翻译:
我们提出了一种基于条件分布模型(如分位数和分布回归)来构造条件有效预测区间的鲁棒方法。我们的方法可以应用于重要的预测问题,包括横截面预测、K步超前预测、综合控制和反事实预测以及个体治疗效果预测。我们的方法利用概率积分变换,并依赖于置换估计秩。与回归残差不同,秩独立于预测因子,允许我们在异方差下构造条件有效的预测区间。我们建立了一致估计下的近似条件有效性,并提供了模型错规格、过拟合和时间序列数据下的近似无条件有效性。我们还提出了一个简单的“形状”调整我们的基线方法,以产生最优的预测间隔。
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英文标题:
《Distributional conformal prediction》
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作者:
Victor Chernozhukov, Kaspar W\"uthrich, Yinchu Zhu
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最新提交年份:
2021
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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英文摘要:
We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression. Our approach can be applied to important prediction problems including cross-sectional prediction, k-step-ahead forecasts, synthetic controls and counterfactual prediction, and individual treatment effects prediction. Our method exploits the probability integral transform and relies on permuting estimated ranks. Unlike regression residuals, ranks are independent of the predictors, allowing us to construct conditionally valid prediction intervals under heteroskedasticity. We establish approximate conditional validity under consistent estimation and provide approximate unconditional validity under model misspecification, overfitting, and with time series data. We also propose a simple "shape" adjustment of our baseline method that yields optimal prediction intervals.
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PDF链接:
https://arxiv.org/pdf/1909.07889