final_program_abstracts[1]

11 IMSC Session Program
Subsampling inference for trends and extremes in climate
data
Thursday - Parallel Session 7
Alexander Gluhovsky
Department of Earth & Atmospheric Sciences, Purdue University, West Lafayette,
USA
Department of Statistics, Purdue University, West Lafayette, USA
Standard statistical methods involve strong assumptions that are rarely met in climate
data, whereas resampling methods permit obtaining valid inference without making
questionable assumptions about the data generating mechanism. Among these,
subsampling works under the weakest assumptions, which makes it particularly
applicable for climate data analyses.
In the talk, two problems will be handled by subsampling techniques. One is 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.
The stochastic one is a zero-mean stationary process (not necessarily an iid noise as is
often assumed). The other problem is the tail index estimation for heavy tailed time
series. Subsampling procedures will be illustrated with modeled and observed data.
This work is supported by the National Science Foundation Grant ATM-0756624.
Abstracts 252