Estimation of STAR–GARCH Models with Iteratively Weighted Least Squares

Conjugate gradient acceleration of iteratively re-weighted least squares methods

Iteratively Re-weighted Least Squares (IRLS) is a method for solving minimization problems involving non-quadratic cost functions, perhaps non-convex and non-smooth, which however can be described as the infimum over a family of quadratic functions. This transformation suggests an algorithmic scheme that solves a sequence of quadratic problems to be tackled efficiently by tools of numerical lin...

Weighted Least Squares Estimation with Missing Data

Iteratively re-weighted least-squares and PEF-based interpolation

Interpolation methods frequently deal poorly with noise. Least-squares based interpolation methods can deal well with noise, as long as it is Gaussian and zero-mean. When this is not the case, other methods are needed. I use an iteratively-reweighted least-squares scheme to interpolate both regular and sparse data with non-stationary prediction-error filters. I show that multi-scale methods are...

Power System State Estimation Using Weighted Least Squares (WLS) and Regularized Weighted Least Squares(RWLS) Method

In this paper, a new formulation for power system state estimation is proposed. The formulation is based on regularized least squares method which uses the principle of Thikonov’s regularization to overcome the limitations of conventional state estimation methods. In this approach, the mathematical unfeasibility which results from the lack of measurements in case of ill-posed problems is elimin...

Improved Iteratively Reweighted Least Squares for Unconstrained

In this paper, we first study q minimization and its associated iterative reweighted algorithm for recovering sparse vectors. Unlike most existing work, we focus on unconstrained q minimization, for which we show a few advantages on noisy measurements and/or approximately sparse vectors. Inspired by the results in [Daubechies et al., Comm. Pure Appl. Math., 63 (2010), pp. 1–38] for constrained ...