Second-order equivalence of rectangular and exponential windows in least-squares estimation of Gaussian autoregressive processes

Exact least-squares algorithms for autoregressive signals can be made to track time-varying parameters by using sliding windows on the data. Two common choices for such windows are the exponential one and the rectangular one. In this paper, it is shown that for Gaussian autoregressive processes, the two types of windows yield approximately the same mean-square error if the windows' parameters are properly chosen.