Project Details
Projekt
Partial least squares for serially dependent data
Applicant
Professorin Dr. Tatyana Krivobokova
Subject Area
Mathematics
Term
from 2011 to 2016
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 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 Programme
Research Units
