The need to extract signals and other components
from time series is a requirement in many empirical sciences, including Medicine,
Engineering, Economics and Climatology, to name but a few. Nowadays, a wide
variety of methods are available, including Wiener--Kolmogorov Filtering, Kalman
Filtering, Local Polynomial Modelling, Principal Components Analysis, and
Wavelet Analysis. Some of the methods have been conceived in the time domain and
others have originated in the frequency domain. A few of the methods may be
interpreted equally in both domains.
Give the variety of the available methods of Statistical Signal Extraction and
Filtering, and given the diversity of the subject areas in which they are
applied, there are plentiful opportunities for cross fertilisation and
technology transfer. We therefore invite the submissions of papers on these
topics which will be organised into an coherent but eclectic group to be
presented at the 3rd IASC World Conference on Computational Statistics and Data
Analysis to be held in Cyprus during October 28-31, 2005.