Partial least squares for serially dependent data

• Antragstellerin: Professorin Dr. Tatyana Krivobokova
• Fachliche Zuordnung: Mathematik
• Förderung: Förderung von 2011 bis 2016
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 40095828

This projects focuses on the partial least squares (PLS) – a type of regularized least squares regression developed for ill-conditioned linear regression models. We seek for the extension of this technique that enables to handle serially dependent data. This extension has to be pursued for both – linear and nonlinear – versions of the PLS algorithm. Thereby, the main focus should be on the asymptotic theory (asymptotic distribution and consistency of the estimators, mean squared prediction error, etc.). The developed method will be applied to the prediction of a specific biological function of a protein based on the collective atomic motion of this protein, both observed over time and known to be highly autocorrelated.

• DFG-Verfahren: Forschungsgruppen
• Teilprojekt zu: FOR 916: Swiss-German Bilateral Research Unit on: Statistical Regularisation and Qualitative Constraints - Inference, Algorithms, Asymptotics and Applications