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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 4
Nonlin. Processes Geophys., 18, 537–544, 2011
https://doi.org/10.5194/npg-18-537-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Recent advances in data analysis and modeling of nonlinear...

Nonlin. Processes Geophys., 18, 537–544, 2011
https://doi.org/10.5194/npg-18-537-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Aug 2011

Research article | 26 Aug 2011

Statistical inference from atmospheric time series: detecting trends and coherent structures

A. Gluhovsky A. Gluhovsky
  • Department of Earth and Atmospheric Sciences and Department of Statistics, Purdue University, West Lafayette, IN 47907, USA

Abstract. Standard statistical methods involve strong assumptions that are rarely met in real data, whereas resampling methods permit obtaining valid inference without making questionable assumptions about the data generating mechanism. Among these methods, subsampling works under the weakest assumptions, which makes it particularly applicable for atmospheric and climate data analyses. In the paper, two problems are addressed using subsampling: (1) the construction of simultaneous confidence bands for the unknown trend in a time series that can be modeled as a sum of two components: deterministic (trend) and stochastic (stationary process, not necessarily an i.i.d. noise or a linear process), and (2) the construction of confidence intervals for the skewness of a nonlinear time series. Non-zero skewness is attributed to the occurrence of coherent structures in turbulent flows, whereas commonly employed linear time series models imply zero skewness.

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