Marina Friedrich, Eric Beutner, Hanno Reuvers, Stephan Smeekes, Jean-Pierre Urbain, Whitney Bader, Bruno Franco, Bernard Lejeune, Emmanuel Mahieu
arXiv 13 Mar 2019 · Statistics — Applications · publishedClimatic Change (2020) · 13 citations (OpenAlex)
arXiv:1903.05403 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Franco, B., Mahieu, E., Emmons, L.K., Tzompa-Sosa, Z.A., Fischer, E.… (2016) Evaluating ethane and methane emissions associated with the development of oil and natural gas extraction in North America self | 0.928 | 4 | 3 | 100% |
| 2 | Franco, B., Bader W., Toon G.C., Bray C., Perrin A., Fischer E.V., S… (2015) Retrieval of ethane from ground-based FTIR solar spectra using improved spectroscopy: Recent burden increase above Jungfraujoch self | 0.874 | 7 | 2 | 100% |
| 3 | Friedrich, M., Smeekes, S. and J.-P. Urbain (2020) Autoregressive wild bootstrap inference for nonparametric trends self | 0.851 | 13 | 5 | 62% |
| 4 | Gardiner, T., Forbes, A. , de Mazière, M., Vigouroux, C., Mahieu, E.… (2008) Trend analysis of greenhouse gases over Europe measuerd by a network of ground-based remote FTIR instruments | 0.811 | 4 | 2 | 100% |
| 5 | Bai, J. and P. Perron (1998) Estimating and testing linear models with multiple structural changes | 0.737 | 3 | 2 | 100% |
| 6 | Kapetanios, G (2008) Bootstrap-based tests for deterministic time-varying coefficients in regression models | 0.644 | 4 | 1 | 100% |
| 7 | Ghosal, S., Sen, A. and A. van der Vaart (2000) Testing Monotonicity of Regression, The Annals of Statistics 28, 1054-1082 | 0.585 | 3 | 1 | 100% |
| 8 | Bühlmann, P (1998) Sieve bootstrap for smoothing in nonstationary time series | 0.511 | 3 | 2 | 33% |
| 9 | Chu, C.-K. and J.S. Marron (1991) Comparison of two bandwidths selectors with dependent errors | 0.511 | 3 | 2 | 33% |
| 10 | Neumann, M.H. and J. Polzehl (1998) Simultaneous bootstrap confidence bands in nonparametric regression | 0.511 | 2 | 2 | 50% |
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