Statistical Signal Extraction and Filtering
Description:
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, Filtering for Non-linear and non-Gaussian Models, Semiparametric Regression, Principal Components Analysis, and Wavelet Analysis.
The track concerns with the use of computational and numerical methods for solving theoretical and practical issues associated with filtering and signal extraction algorithms, the impact of the techniques on the relevant subject areas, and specific applications involving computing and data analysis.
Full papers containing a strong computational or data analytic component will be considered for publication in the third Special Issue on Statistical Signal Extraction and Filtering of the journal Computational Statistics and Data Analysis. All submissions must contain original unpublished work not being considered for publication elsewhere. Submissions will be refereed according to standard procedures for CSDA.
Co-Chairs:
- D.S.G. (Stephen) Pollock, Queen Mary University of London, UK.
- Tommaso Proietti, University of Roma "Tor Vergata", Italy.
- Esther Ruiz, Universidad Carlos III, Madrid, Spain.
Created by Computing & Statistics 2007