final_program_abstracts[1]

11 IMSC Session Program
The analysis of long memory climate series
Wednesday - Plenary Session 10
Peter F. Craigmile
Department of Statistics, The Ohio State University, Columbus, USA
Long memory processes are a class of stochastic models that exhibit a slowly
decaying autocorrelation sequence or equivalently a spectral density function with a
pole at zero frequency. Starting with its application in hydrology by Hurst (1951),
long memory processes have been used in many applications including atmospheric
and climate sciences. It is very common for example to model temperature time series
observed at a single location using a long memory process.
In this talk we will review the properties of such processes and will discuss methods
that are commonly employed in their statistical analysis. We will discuss the impact
of long memory dependence upon trend estimation, and will extend our discussion to
the analysis of long memory dependence observed in space-time series. We will
motivate this topic via an analysis of temperature series taken from a number of
different monitoring stations.
Abstracts 158