 |
| Titel |
Statistical inference from atmospheric time series: detecting trends and coherent structures |
| VerfasserIn |
A. Gluhovsky |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1023-5809
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 4 ; Nr. 18, no. 4 (2011-08-26), S.537-544 |
| Datensatznummer |
250013949
|
| Publikation (Nr.) |
 copernicus.org/npg-18-537-2011.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|