In this contribution we extend our previous results on the structured total least squares problem to the case of weighted cost functions. It is shown that the computational complexity of the proposed algorithm is preserved linear in the sample size when the weight matrix is banded with bandwidth that is independent of the sample size.

The paper reveals that the Weighted Logarithmic Least Squares Method used for deriving evaluations of alternatives from incomplete pairwise comparison matrices (iWLLSM) coincides with the recursive Buchholz ranking method applied in generalized tournaments, despite their different approach and calculation. We study it with respect to a set of new properties and present the strength of the metho...

Using the -norm to regularize the least-squares criterion, the batch least-absolute shrinkage and selection operator (Lasso) has well-documented merits for estimating sparse signals of interest emerging in various applications where observations adhere to parsimonious linear regression models. To cope with high complexity, increasing memory requirements, and lack of tracking capability that bat...

This work provides conditions on the input sequence that ensure the exponential asymptotic stability of the inverse of the forward prediction error filter obtained by means of the Recursive Weighted Least Squares algorithm. Note that this filter is in general time varying. Thus this result is a natural extension to the well-known minimum phase property of forward prediction error filters obtain...

This paper introduces locally weighted temporal difference learning for evaluation of a class of policies whose value function is nonlinear in the state. Least squares temporal difference learning is used for training local models according to a distance metric in state-space. Empirical evaluations are reported demonstrating learning performance on a number of strongly non-linear value function...

I show that important conclusions about time-series return predictability change when using least squares estimates weighted by ex-ante return variance (WLS-EV) instead of OLS. In small-sample simulations, WLS-EV results in large efficiency gains relative to OLS, fewer false negatives, and avoids the bias associated with ex-post weighting schemes. Empirically, traditional predictors such as the...

Variable selection is important in fine tuning partial least squares (PLS) regression models. This study introduces a novel variable weighting method for PLS regression where the univariate response variable y is used to guide the variable weighting in a recursive manner—the method is called recursive weighted PLS or just rPLS. The method iteratively reweights the variables using the regression...

Some recursive least-squares algorithms for multichannel active noise control have recently been introduced, including computationally efficient (i.e. “fast”) versions. However, these previously published algorithms suffer from numerical instability due to finite precision computations. In this paper, numerically robust recursive least-squares algorithms for multichannel active noise control sy...

BACKGROUND AND PURPOSE The pathophysiology of eRLS has not yet been elucidated. The purpose of the study was to assess, in patients with eRLS, the volume, iron content, and activation of the brain during night-time episodes of SLD and PLMs. MATERIALS AND METHODS Eleven right-handed unmedicated patients with eRLS (mean age, 55.3 ± 8.4 years; disease duration, 17.5 ± 14.05 years) and 11 matched...