摘要翻译：
为了量化条件对象的点估计（如条件均值或方差）的不确定性，必须考虑参数不确定性。考虑参数不确定性的尝试通常基于观察两个独立过程的不切实际的假设，其中一个用于参数估计，另一个用于条件化。这种不切实际的基础提出了一个问题，即这些间隔在现实环境中是否在理论上是合理的。本文给出了这类区间的渐近证明，它不需要这样不切实际的假设，而是依赖于样本分裂方法。通过证明我们的样本分裂区间与标准区间渐近重合，我们为条件对象的置信区间提供了一个新颖而现实的证明。对一类丰富的时间序列模型进行了分析。
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英文标题：
《A Justification of Conditional Confidence Intervals》
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作者：
Eric Beutner, Alexander Heinemann and Stephan Smeekes
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最新提交年份：
2019
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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：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  To quantify uncertainty around point estimates of conditional objects such as conditional means or variances, parameter uncertainty has to be taken into account. Attempts to incorporate parameter uncertainty are typically based on the unrealistic assumption of observing two independent processes, where one is used for parameter estimation, and the other for conditioning upon. Such unrealistic foundation raises the question whether these intervals are theoretically justified in a realistic setting. This paper presents an asymptotic justification for this type of intervals that does not require such an unrealistic assumption, but relies on a sample-split approach instead. By showing that our sample-split intervals coincide asymptotically with the standard intervals, we provide a novel, and realistic, justification for confidence intervals of conditional objects. The analysis is carried out for a rich class of time series models.
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PDF链接：
https://arxiv.org/pdf/1710.00643