Motivation

The analysis of dependent data is nowadays of extremely high importance. While the world is expanding in terms of virtual connections, the presence of sensors is spreading globally reporting thousands of measurements each second. Large amounts of data presenting some form of time and/or spatial dependence are produced every day. These measurements can come, for example, from smart-meters in plants to smart-shirts for athletes. Almost every object in our everyday life has the ability to deliver data. Several statistical tools have been developed by the members of the Lab for extracting information from large and complex structures of dependent data.

Publications

• Guerrier, S., Molinari, R. & Stebler, Y., “Theoretical Limitations of Allan Variance-based Regression for Time Series Model Estimation”, IEEE Signal Processing Letters, 2016. Link Full text
• Guerrier, S., Molinari, R. and Victoria-Feser, M.-P. Estimation of Time Series via Robust Wavelet Variance, Austrian Journal of Statistics, 2014. Link Full text
• Guerrier, S., Stebler, Y., Skaloud, J. & Victoria-Feser, M.-P., Wavelet-Variance-Based Estimation for Composite Stochastic Processes, Journal of the American Statistical Association (Theory & Methods), 2013. Link Full text

Conference Proceedings