Partial least squares for serially dependent data

这个项目的重点是偏最小二乘(PLS) -一种正则化最小二乘回归开发的病态线性回归模型。我们寻求对该技术的扩展，使其能够处理串行依赖的数据。这种扩展必须追求的-线性和非线性版本的PLS算法。因此，重点应该放在渐近理论上（估计量的渐近分布和一致性，均方预测误差等）。所开发的方法将应用于基于该蛋白质的集体原子运动的蛋白质的特定生物学功能的预测，这些蛋白质随时间的推移被观察到并且已知是高度自相关的。

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.