Weighted least-squares and quasilikelihood estimation for categorical data under singular models

Weighted Least Squares Estimation with Missing Data

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...

Weighted Majorization Algorithms for Weighted Least Squares Decomposition Models

For many least-squares decomposition models efficient algorithms are well known. A more difficult problem arises in decomposition models where each residual is weighted by a nonnegative value. A special case is principal components analysis with missing data. Kiers (1997) discusses an algorithm for minimizing weighted decomposition models by iterative majorization. In this paper, we propose a m...

Least Squares Parameter Estimation, Tiknonov Regularization, and Singular Value Decomposition

This handout addresses the errors in parameters estimated from fitting a function to data. Any sample of measured quantities will naturally contain some variability. Normal variations in data propagate through any equation or function applied to the data. In general we may be interested in combining the data in some mathematical way to compute another quantity. For example , we may be intereste...

Weighted Local Least Squares Imputation Method for Missing Value Estimation

Missing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation (LLSI) method for estimating the missing values. In this paper, we propose a Weighted Local Least Sq...