大气中乙烷含量时间趋势的统计分析

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摘要翻译：
乙烷是地球大气中含量最丰富的非甲烷碳氢化合物，是通过各种化学途径形成对流层臭氧的重要前体。乙烷也是一种间接温室气体（全球升温潜能值），通过消耗羟基自由基(OH)影响甲烷的大气寿命。因此，了解趋势的发展和查明大气中乙烷的趋势逆转是至关重要的。我们的数据集由地基FTIR测量获得的四个日乙烷柱系列组成。与许多其他十年时间序列一样，我们的数据具有自相关、异方差和季节效应的特征。此外，由于仪器故障或不利的测量条件而导致的观测缺失在这类系列中也很常见。因此，本文的目标是利用正确处理这些数据特征的统计工具来分析大气乙烷的趋势。我们提出了为分析时间趋势和趋势反转而设计的选定方法。我们考虑了破碎的线性趋势和平滑变化的非线性趋势的bootstrap推理。特别地，对于断裂趋势模型，我们提出了一种bootstrap方法来推断断裂的位置和相应的斜率变化。对于平滑趋势模型，我们在非参数估计的趋势周围构造了同时的置信带。我们的自回归wild bootstrap方法，结合季节性过滤器，能够处理上面提到的所有问题。
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英文标题：
《A statistical analysis of time trends in atmospheric ethane》
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作者：
Marina Friedrich, Eric Beutner, Hanno Reuvers, Stephan Smeekes,
  Jean-Pierre Urbain, Whitney Bader, Bruno Franco, Bernard Lejeune, Emmanuel
  Mahieu
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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：
  Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns obtained from ground-based FTIR measurements. As many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity, and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. The goal of this paper is therefore to analyze trends in atmospheric ethane with statistical tools that correctly address these data features. We present selected methods designed for the analysis of time trends and trend reversals. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above.
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PDF链接：
https://arxiv.org/pdf/1903.05403

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关键词：统计分析 时间趋势 计分析 econometrics parametrical 断裂 through 能够 提出 季节性