This work proposes a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed sequentially minimizes the time-continuous full-order-model residual within low-dimensional space–time trial subspace over time windows. comprises generalization existing approaches, as particular instances methodology recover Galerkin, Petrov–Galerkin (LSPG), and LSPG projection. I...

We consider a linear model with normally distributed but heteroscedastic errors. When the error variances are functionally related to the regression parameter, one can use either maximum likelihood or generalized least squares to estimate the regression parameter. We show that maximum likelihood is much more sensitive to small misspecifications in the functional relationship between the error v...

This paper deals with the design and identification of the dynamic model of a compactor, an articulated frame steering mobile engine for use in road construction. A classical approach based on an Ordinary Least Squares identification method is used. A survey of these techniques is given and applied to estimate dynamic parameters of the compactor, and especially the contribution of the contact s...

We study the asymptotic behavior of the least squares estimators of the unknown parameters of bifurcating autoregressive processes. Under very weak assumptions on the driven noise of the process, namely conditional pair-wise independence and suitable moment conditions, we establish the almost sure convergence of our estimators together with the quadratic strong law and the central limit theorem...