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摘要翻译：
在本文中，我们提出了一个自回归野生自举方法来构造一个光滑的确定性趋势周围的置信带。自举方法易于实现，在数据丢失的情况下不需要任何调整，这使得它特别适合于气候学应用。在一般条件下，我们建立了bootstrap方法在逐点置信带和同时置信带上的渐近有效性，考虑了数据缺失、序列依赖和异方差的一般模式。通过仿真研究了该方法的有限样本性质。我们用该方法研究了瑞士阿尔卑斯山气象站每天测量的大气乙烷的变化趋势，该方法可以很容易地处理由于不利天气条件而丢失的许多观测。
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英文标题：
《Autoregressive Wild Bootstrap Inference for Nonparametric Trends》
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作者：
Marina Friedrich, Stephan Smeekes, Jean-Pierre Urbain
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最新提交年份：
2019
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分类信息：
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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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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英文摘要：
In this paper we propose an autoregressive wild bootstrap method to construct confidence bands around a smooth deterministic trend. The bootstrap method is easy to implement and does not require any adjustments in the presence of missing data, which makes it particularly suitable for climatological applications. We establish the asymptotic validity of the bootstrap method for both pointwise and simultaneous confidence bands under general conditions, allowing for general patterns of missing data, serial dependence and heteroskedasticity. The finite sample properties of the method are studied in a simulation study. We use the method to study the evolution of trends in daily measurements of atmospheric ethane obtained from a weather station in the Swiss Alps, where the method can easily deal with the many missing observations due to adverse weather conditions.
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PDF链接：
https://arxiv.org/pdf/1807.02357
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